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Learning-based techniques for heart disease prediction: a survey of models and performance metrics

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Abstract

Heart disease (HD) is a major threat to human health, and the medical field generates vast amounts of data that doctors struggle to effectively interpret and use. Early prediction and classification of HD types are crucial for effective medical treatment. Researchers have found it important to use learning-based techniques from machine and deep learning, such as supervised and deep neural networks, to develop automatic models for HD. These techniques have been used to simulate HD management and extract important features from complex data sets. This survey examines various HD prediction models, classifying the learning-based techniques, datasets, and contexts used, and analyzing the performance metrics of each contribution. It also clarifies which method suits a type of HD. With the growth of data sets, researchers are increasingly utilizing these techniques to create more precise models. However, there is still much work to be done to improve the accuracy of HD predictions.

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Data Availibility Statement

Data sharing is not applicable to this article as no new data were generated or analyzed during the current study.

Abbreviations

ACO:

Ant Colony Optimization

AI:

Artificial Intelligence

AL:

Active Learning

ANN:

Artificial Neural Network

AUC:

Area Under Curve

CA:

Cardiac Arrhythmia

CAD:

Coronary Artery Disease

CART:

Classification and Regression Trees

CHD:

Coronary Heart Disease

CM:

Cardiomyopathy

CNHD:

Congenital Heart Disease

CVD:

Cardiovascular Diseases

DBSCAN:

Density-Based Spatial Clustering of Applications with Noise

DL:

Deep Learning

DLT:

Deep Learning Techniques

DT:

Decision Trees

EL:

Evolutionary Learning

FL:

Fuzzy Logic

FS:

Feature Selection

GA:

Genetic Algorithm

GB:

Gradient Boosting

HD:

Heart Disease

HF:

Heart Failure

ISO DATA:

Iterative Self-Organizing Data

KM:

K-Means

KNN:

K-Nearest Neighbor

LBTs:

Learning-Based Techniques

LDA:

Latent Dirichlet Allocation

LIMBO:

ScaLable InforMation Bottleneck

LiR:

Linear Regression

LR :

Logistic Regression

LSTM:

Long Short-Term Memory

MI:

Myocardial Infarction

ML:

Machine Learning

MLP:

Multi Layer Perceptron

NB:

Naive Bayes

NN:

Neural Network

PAD:

Peripheral Artery Disease

PCA:

Principal Component Analysis

PD:

Pericardial Disease

PSO:

Particle Swarm Optimization

RBFN:

Radial Basis Function Network

RFA:

Random Forest Algorithm

RHD:

Rheumatic Heart Disease

RL:

Reinforcement Learning

RNN:

Recurrent Neural Network

ROC:

Receiver Operating Characteristic Curve

ROCK:

Chameleon Robust Clustering using links

SHD:

Structural Heart Disease

SL:

Supervised Learning

SMOTE:

Synthetic Minority Over-Sampling Technique

SSL:

Semi-Supervised Learning

SVM:

Support Vector Machine

UCI:

University of California Irvine

USL:

Unsupervised Learning

VD:

Vascular Disease

XGB:

Extreme Gradient Boosting

AD:

Anomaly Detection

References

  1. Cdc (2022) Centers for disease control and prevention. Leading causes of death in United States of America. https://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm. Accessed December 2022

  2. De-Backer G, Ambrosioni E, Borch-Johnsen K et al (2004) European guidelines on cardiovascular disease prevention in clinical practice; third joint task force of european and other societies on cardiovascular disease prevention in clinical practice. Eur J Preventive Cardiol 97(10):1019–30

    Google Scholar 

  3. Geron A (2019) Hands-on machine learning with Scikit-learn, Keras, and TensorFlow. 2nd Edition O’Reilly Media, Inc

  4. Jain A, Murty M, Flynn P (1999) An introduction to clustering algorithms for data analysis. In: ACM computing surveys. https://doi.org/10.1145/331499.331504

  5. Jolliffe IT, Cadima J (2016) Principal component analysis: a review and recent developments. Philos Trans Series A, Math Phys Eng Sci 374. https://doi.org/10.1098/rsta.2015.0202

  6. Marshland S (2009) Machine learning an algorithmic perspective. CRC Press, pp 6–7

  7. Kononenko I (2001) Machine learning for medical diagnosis: history, state of the art and perspective. Artificial intelligence in medicine 23(1):89–109. https://doi.org/10.1016/S0933-3657(01)00077-X

    Article  Google Scholar 

  8. Rambhajani M, Deepanker W, Pathak N (2015) A survey on implementation of machine learning techniques for dermatology diseases classification. Int J Adv Eng Technol 23:194–202

    Google Scholar 

  9. Banu NK, Swamy S (2016) Prediction of heart disease at early stage using data mining and big data analytics: a survey. In ICEECCOT: 2016 international conference on electrical, electronics, communication, computer and optimization techniques, pp 256–261. https://doi.org/10.1109/ICEECCOT.2016.7955226

  10. Hazra A, Mandal S, Gupta A, Mukherjee A (2017) Heart disease diagnosis and prediction using machine learning and data mining techniques: a review. Adv Comput Sci Technol 10:2137–2159

    Google Scholar 

  11. Fatima M, Pasha M (2017) Survey of machine learning algorithms for disease diagnostic. J Intell Learn Syst Appl 09:1–16

    Google Scholar 

  12. Safdar S, Zafar S, Zafar N, Khan NF (2018) Machine learning based decision support systems for heart disease diagnosis: a review. Artif Intell Rev 50(4):597–623. https://doi.org/10.1007/s10462-017-9552-8

    Article  Google Scholar 

  13. Ramalingam VV, Dandapath A, Raja MK (2018) Heart disease prediction using machine learning techniques: a survey. Int J Eng Technol 7(2.8):684–687. https://doi.org/10.14419/ijet.v7i2.8.10557

  14. Khan Y, Qamar U, Yousaf N, Khan A (2019) Machine learning techniques for heart disease datasets: a survey. International conference on machine learning and computing, pp 27–35

  15. Murat F, Yildirim O, Talo M et al (2020) Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med 120:103726. https://doi.org/10.1016/j.compbiomed.2020.103726

  16. Kaur PC (2020) A study on role of machine learning in detectin heart disease. In 2020 Fourth international conference on computing methodologies and communication. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-00037

  17. Limbitote M, Mahajan D, Damkondwar K, Patil P (2020) A survey on prediction techniques of heart disease using machine learning. Int J Eng Res Technol 9. https://doi.org/10.17577/IJERTV9IS060298

  18. Jayatilake SMDA, Ganegoda GU (2021) Involvement of machine learning tools in healthcare decision making. J Healthcare Eng 2021:20. https://doi.org/10.1155/2021/6679512

  19. Calamuneri A, Donato L, Scimone C et al (2017) On machine learning in biomedicine. Life Safety Secur 5(12):96–99. https://doi.org/10.12882/2283-7604.2017.5.12

  20. Bharat A, Pooja N, Reddy RA (2018) Using machine learning algorithms for breast cancer risk prediction and diagnosis. In 2018 3rd international conference on circuits, control, communication and computing (I4C ), pp1–4. https://doi.org/10.1109/CIMCA.2018.8739696

  21. Mishra V, Singh Y, Rath SK (2019) Breast cancer detection from thermograms using feature extraction and machine learning techniques. In 2019 IEEE 5th international conference for convergence in technology (ICCT), pp 1–5. https://doi.org/10.1109/I2CT45611.2019.9033713

  22. Murphy K P (2012) Machine learning: a probabilistic perspective. London, pp 347–349

  23. Hosmer DW, Lemeshow S, Rodney X (2013). Applied logistic regression. https://doi.org/10.1002/9781118548387

  24. Kamalalochana N, Guptha NS (2019) Optimizing random forest to detect disease in apple leaf. Int J Eng Adv Technol 8:244–249

    Article  Google Scholar 

  25. Celebi ME, Aydin K (2016). Unsupervised learning algorithms. https://doi.org/10.1007/978-3-319-24211-8

  26. Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press

    Book  Google Scholar 

  27. Han J, Pei J, Kamber M (2012) Data mining: concepts and techniques, pp 173–175. https://doi.org/10.1016/C2009-0-61819-5

  28. Sowmya SLK, Guptha NS, Shruthi G, Thanuja K, Anitha K (2019) Detection of liver lesion using ROBUST machine learning technique. Int J Eng Adv Technol (IJEAT) 8:214–219

    Article  Google Scholar 

  29. Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics Rev Pub Radiological Soc North America Inc 37(2):505–515. https://doi.org/10.1148/rg.2017160130

  30. Pr R, Nair RS, Veena G (2019) A comparative study of lung cancer detection using machine learning algorithms. In 2019 IEEE international conference on electrical, computer and communication technologies (ICECCT), pp 1–4. https://doi.org/10.1109/ICECCT.2019.8869001

  31. Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920–30. https://doi.org/10.1161/circulationaha.115.001593

    Article  Google Scholar 

  32. Andreopoulos B, An A, Wang X, Schroeder M (2009) A roadmap of clustering algorithms: finding a match for a biomedical application. Briefings Bioinformat 10(3):297–314. https://doi.org/10.1093/bib/bbn058

    Article  Google Scholar 

  33. Latif J, Xiao C, Imran A, Tu S (2019) Medical imaging using machine learning and deep learning algorithms: a review. In 2019 2nd international conference on computing, mathematics and engineering technologies (iCoMET). https://doi.org/10.1109/ICOMET.2019.8673502

  34. Princy RJP, Parthasarathy S, Jose SH et al (2020) Prediction of cardiac disease using supervised machine learning algorithms. 2020 4th international conference on intelligent computing and control systems (ICICCS), pp 570–575. https://doi.org/10.1109/ICICCS48265.2020.9121169

  35. Patel JM, TejalUpadhyay S, Patel S (2016) Heart disease prediction using machine learning and data mining technique. Int J Comput Sci Commun 7:129–137

    Google Scholar 

  36. Krishnan JS, Geetha S (2019) Prediction of heart disease using machine learning algorithms. 2019 1st international conference on innovations in information and communication technology (ICIICT), pp 1–5. https://doi.org/10.1109/ICIICT1.2019.8741465

  37. Almustafa KM (2020) Prediction of heart disease and classifiers’ sensitivity analysis. BMC Bioinformatics 21. https://doi.org/10.1186/s12859-020-03626-y

  38. Asadi S, Roshan SE, Kattan MW (2021) Random forest swarm optimization-based for heart diseases diagnosis. J Biomed Informatics 115:103690. https://doi.org/10.1016/j.jbi.2021.103690

  39. Krishna Prasad K, Aithal PS, Bappalige NN, Soumya S (2021) An integration of cardiovascular event data and machine learning models for cardiac arrest predictions. Int J Health Sci Pharm 5(1). https://doi.org/10.47992/IJHSP.2581.6411.0061

  40. Rakshit T, Shrestha A (2021) Comparative analysis and implementation of heart stroke prediction using various machine learning techniques. Int J Eng Tech Res 10(6):886–890

    Google Scholar 

  41. Gupta N, Dharmale G, Parmar D (2021) Heart disease prediction using machine learning. J Emerg Technol Innovative Res. https://doi.org/10.13140/RG.2.2.16604.92800

  42. Trisal A, Sagar V, Jameel R (2022) Cardiac disease prediction using machine learning algorithms. In: 2022 international conference on computational intelligence and sustainable engineering solutions (CISES), pp 583–589. https://doi.org/10.1109/CISES54857.2022.9844370

  43. Sateesh C, Balamanigandan R (2022) Heart disease prediction using innovative decision tree technique for increasing the accuracy compared with convolutional neural networks. In: 2022 2nd international conference on innovative practices in technology and management (ICIPTM) 2, pp 583–587. https://doi.org/10.1109/ICIPTM54933.2022.9754196

  44. Enriko IKA, Suryanegara M, Gunawan D (2016) Heart disease prediction system using k-Nearest neighbor algorithm with simplified patient’s health parameters. J Telecommun Electron Comput Eng 8(12):59–65

    Google Scholar 

  45. Khateeb N, Usman M (2017) Efficient heart disease prediction system using K-nearest neighbor classification technique. BDIOT2017: Proceedings of the international conference on big data and internet of thing, pp 21–26. https://doi.org/10.1145/3175684.3175703

  46. Ma J (2017) Prediction of heart disease using k-nearest neighbor and particle swarm optimization. Biomed Res-tokyo 28:4154–4158

    Google Scholar 

  47. Chakarverti M, Yadavand S, Rajan R (2019) Classification technique for heart disease prediction in data mining. In: 2019 2nd international conference on intelligent computing, instrumentation and control technologies, vol 1, pp 1578–1582. https://doi.org/10.1109/ICICICT46008.2019.8993191

  48. Basha N, Ashokkumar PS, Krishna GC et al (2019) Early detection of heart syndrome using machine learning technique. In: 2019 4th international conference on electrical, electronics, communication, computer technologies and optimization techniques (ICEECCOT), pp 387–391. https://doi.org/10.1109/ICEECCOT46775.2019.9114651

  49. Singh A, Kumar RR (2020) Heart disease prediction using machine learning algorithms. In: 2020 international conference on electrical and electronics engineering (ICE3), pp 452–457. https://doi.org/10.1109/ICE348803.2020.9122958

  50. Shah D, Patel SB, Bharti SK (2020) Heart disease prediction using machine learning techniques. SN Comput Sci 1:345. https://doi.org/10.1007/s42979-020-00365-y

  51. Rajathi S, Radhamani G (2016) Prediction and analysis of rheumatic heart disease using kNN classification with ACO. In: 2016 international conference on data mining and advanced computing (SAPIENCE), pp 68–73. https://doi.org/10.1109/SAPIENCE.2016.7684132

  52. Jha k k, Jha A, Rathore K et al (2021) Forecasting of heart diseases in early stages using machine learning approaches. In: 2021 international conference on forensics, analytics, big data, security (FABS), vol 1, pp 1–5. https://doi.org/10.1109/FABS52071.2021.9702665

  53. Garg A, Sharma B, Khan R (2021) Heart disease prediction using machine learning techniques. IOP Conference Series: Mater Sci Eng 1022(1):012046. https://doi.org/10.1088/1757-899X/1022/1/012046

    Article  Google Scholar 

  54. Jindal H, Agrawal S, Rishabh K et al (2021) Heart disease prediction using machine learning algorithms. IOP Conference Series: Mater Sci Eng 1022(1):012072. https://doi.org/10.1088/1757-899X/1022/1/012072

  55. Roy RE, Kulkarni P, Kumar S (2022) Machine learning techniques in predicting heart disease a survey. 2022 IEEE world conference on applied intelligence and computing (AIC), pp 373–377. https://doi.org/10.1109/AIC55036.2022.9848945

  56. Shahiduzzaman M, Biswas NH, Momin M et al (2022) Prognosis of cardiovascular disease using machine learning procedures. In: 2022 international conference on advancement in electrical and electronic engineering (ICAEEE), pp 1–6. https://doi.org/10.1109/ICAEEE54957.2022.9836418

  57. Singh Y, Sinha NS, Singh SK (2017) Heart disease prediction system using random forest. International conference on advances in computing and data sciences. https://doi.org/10.1007/978-981-10-5427-3-63

  58. Reddy NCS, Nee SS, Min LZ et al (2019) Classification and feature selection approaches by machine learning techniques: heart disease prediction. Int J Innovative Comput. https://doi.org/10.11113/IJIC.V9N1.210

  59. Krishnani D, Kumari A, Dewangan A et al (2019) Prediction of coronary heart disease using supervised machine learning algorithms. In: 2019 IEEE region 10 conference (TENCON), pp 367–372. https://doi.org/10.1109/TENCON.2019.8929434

  60. Obasi T, Shafiq MO (2019) Towards comparing and using machine learning techniques for detecting and predicting heart attack and diseases. in: 2019 IEEE international conference on big data, pp 2393–2402. https://doi.org/10.1109/BigData47090.2019.9005488

  61. Ahmed H, Younis MG, Hendawi AM et al (2020) Heart disease identification from patients social posts, machine learning solution on Spark. Future Generation Computer Syst 111:714–722. https://doi.org/10.1016/j.future.2019.09.056

  62. Kumar NK, Sindhu GS, Prashanthi DK et al (2020) Analysis and prediction of cardio vascular disease using machine learning classifiers. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp 15–21. https://doi.org/10.1109/ICACCS48705.2020.9074183

  63. Alim M A, Habib S, Farooq Y, Rafay A (2020) Robust heart disease prediction: a novel approach based on significant feature and ensemble learning model. In: 2020 3rd international conference on computing, mathematics and engineering technologies (iCoMET), pp 1–5. https://doi.org/10.1109/iCoMET48670.2020.9074135

  64. Vinayaka S, Gupta PK (2020) Heart disease prediction system using classification algorithms. Adv Comput Data Sci 395–404. https://doi.org/10.1007/978-981-15-6634-9.36

  65. Alqahtani LA, Alotaibi HM, Khan IU, Aslam N (2020) Automated prediction of heart disease using optimized machine learning techniques. In: 020 11th IEEE Annual ubiquitous computing, electronics and mobile communication conference (UEMCON), pp 0298–0302. https://doi.org/10.1109/UEMCON51285.2020.9298051

  66. Kumar KL, Kousar GN, Madhurya JA (2020) Coronary artery disease prediction using data mining techniques. In: 2020 3rd international conference on intelligent sustainable systems (ICISS), pp 693–697. https://doi.org/10.1109/ICISS49785.2020.9316014

  67. Liang PY, Wang LJ, Wu YS, Pai TW, Wang CH, Liu MH (2020) Prediction of patients with heart failure after myocardial infarction. In: 2020 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 2009–2014. https://doi.org/10.1109/BIBM49941.2020.9313253

  68. Ahmad GN, Ullah S, Algethami AA, Fatima H, Akhter SMH (2022) Comparative study of optimum medical diagnosis of human heart disease using machine learning technique with and without sequential feature selection. IEEE Access 10:23808–23828. https://doi.org/10.1109/ACCESS.2022.3153047

  69. Rajdhan A, Agarwal A, Sai M, Ravi D, Ghuli P (2020) Heart disease prediction using machine learning. Int J Eng Res Technol. https://doi.org/10.17577/IJERTV9IS040614

  70. Bindhika GSS, Meghana M, Reddy MS, Rajalakshmi (2020) Heart disease prediction using machine learning techniques. Int Res J Eng Technol 7

  71. Sharma V, Yadav S, Gupta M (2020) Heart disease prediction using machine learning techniques. In: 2020 2nd international conference on advances in computing, communication control and networking (ICACCCN), pp 177–181. https://doi.org/10.1109/ICACCCN51052.2020.9362842

  72. Battula K, Durgadinesh R, Suryapratap K, Vinaykumar G (2021) Use of machine learning techniques in the prediction of heart disease. In: 2021 international conference on electrical, computer, communications and mechatronics engineering (ICECCME), pp 1–5. https://doi.org/10.1109/ICECCME52200.2021.9591026

  73. Nissa N, Jamwal S, Ganie S (2021) Heart disease prediction using machine learning techniques. Wesleyan J Res 13(67)

  74. Mukherjee B, Roy S, Sarobin VR (2021) Application of machine learning algorithm for cardiovascular disease detection. In: 2021 Fifth international conference on i-smac (iot in social, mobile, analytics and cloud) (I-SMAC), pp 456–463. https://doi.org/10.1109/I-SMAC52330.2021.9640932

  75. Kumar D, Chaman C, Gupta A, Raboaca MS, Bakariya B (2021) Detection of cardiac disease and association with family history using machine learning. In: 2021 10th international conference on system modeling and advancement in research trends (SMART), pp 670–675. https://doi.org/10.1109/SMART52563.2021.9676234

  76. Akter S, Amina M, Mansoor N (2021) Early diagnosis and comparative analysis of different machine learning algorithms for myocardial infarction prediction. In: 2021 IEEE 9th region 10 humanitarian technology conference (R10-HTC), pp 01–06. https://doi.org/10.1109/R10-HTC53172.2021.9641080

  77. Williams R, Shongwe T, Hasan AN, Rameshar V (2021) Heart disease prediction using machine learning techniques. In: 2021 international conference on data analytics for business and industry (ICDABI), pp 118–123. https://doi.org/10.1109/ICDABI53623.2021.9655783

  78. Singh H, Gupta T, Sidhu J (2021) Prediction of heart disease using machine learning techniques. sixth international conference on image information processing (ICIIP), vol 6, pp 164–169. https://doi.org/10.1109/ICIIP53038.2021.9702625

  79. Pal M, Parija S (2021) Prediction of heart diseases using random forest. J Phys Conference Series 1817. https://doi.org/10.1088/1742-6596/1817/1/012009

  80. Tr R, Kumar L, Simaiya S, Kaur A, Hamdi M (2022) Predictive analysis of heart diseases with machine learning approaches. Malaysian J Comput Sci 2022:132–148. https://doi.org/10.22452/mjcs.sp2022no1.10

  81. Mahaveer, Puneet, Deepika (2022) Cardiovascular disease Prediction analysis using classification techniques. 2022 IEEE Delhi section conference (DELCON), pp 1–6

  82. Swain D, Parmar B, Shah H, Gandhi A, Pradhan M, Kaur H, Acharya B (2022) Cardiovascular disease prediction using various machine learning algorithms. J Comput Sci 18:993–1004. https://doi.org/10.3844/jcssp.2022.993.1004

  83. Nabeel M, Awan MJ, Raza M, Muslih H, Majeed S (2021) Heart attack disease data analytics and machine learning. In: 2021 international conference on innovative computing (ICIC), pp 1–6. https://doi.org/10.1109/ICIC53490.2021.9692977

  84. Kohli PS, Arora S (2018) Application of Machine Learning in Disease Prediction. 2018 4th International conference on computing communication and automation (ICCCA), pp 1–4. https://doi.org/10.1109/CCAA.2018.8777449

  85. Bizimana PC, Zhang Z, Asim M, El-Latif AA An Effective machine learning-based model for an early heart disease prediction. BioMed Res Int 2023:11. https://doi.org/10.1155/2023/3531420

  86. Saw M, Saxena T, Kaithwas S, Yadav R, Lal N (2020) Estimation of prediction for getting heart disease using logistic regression model of machine learning. In: 2020 international conference on computer communication and informatics (ICCCI), pp 1–6. https://doi.org/10.1109/ICCCI48352.2020.9104210

  87. Thakkar HK, Shukla H, Patil S (2020) A comparative analysis of machine learning classifiers for robust heart disease prediction. In: 2020 IEEE 17th India council international conference (INDICON), pp 1–6. https://doi.org/10.1109/INDICON49873.2020.9342444

  88. Dinesh KG, Arumugaraj K, Santhosh K, Mareeswari V (2018) Prediction of cardiovascular disease using machine learning algorithms. In: 2018 international conference on current trends towards converging technologies (ICCTCT), pp 1–7. https://doi.org/10.1109/ICCTCT.2018.8550857

  89. Chauhan Y (2020) Cardiovascular disease prediction using classification algorithms of machine learning. Int J Sci Res (IJSR). https://doi.org/10.21275/SR20501193934

  90. Pandian AK, Kumar TS, Dhandare SP, Thabasum AS (2021) Development and deployment of a machine learning model for automatic heart failure prediction. In: 2021 Asian conference on innovation in technology (ASIANCON), pp 1–6. https://doi.org/10.1109/ASIANCON51346.2021.9544787

  91. Sunetha J, Tabassum H, Chavan P, Deepak NR (2022) Prediction of cardiac diseases using machine learning algorithms. In: Reddy V, soft computing and signal processing, pp 11–22

  92. Gupta C, Saha A, Reddy NVS, Acharya UD (2022) Cardiac disease prediction using supervised machine learning techniques. J Phys: Conference Series 2161. https://doi.org/10.1088/1742-6596/2161/1/012013

  93. Tsarapatsani K, Sakellarios AI, Pezoulas VC et al (2022) Machine learning models for cardiovascular disease events prediction. In: 2022 44th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 1066–1069. https://doi.org/10.1109/embc48229.2022.9871121

  94. Vayadande KB, Golawar R, Khairnar S, Dhiwar A, Dhiwar S, Bhoite S, Khadke D (2022) Heart disease prediction using machine learning and deep learning algorithms. In: 2022 international conference on computational intelligence and sustainable engineering solutions (CISES), pp 393–401

  95. Gupta A, Kumar L, Jain R, Nagrath P (2019) Heart disease prediction using classification (Naive Bayes). Proceedings of first international conference on computing, communications, and cyber-security (IC4S 2019). https://doi.org/10.1007/978-981-15-3369-3_42

  96. Karthick D, Priyadharshini B (2018) Predicting the chances of occurrence of Cardio Vascular Disease (CVD) in people using classification techniques within fifty years of age. In: 2018 2nd international conference on inventive systems and control (ICISC), pp 1182–1186. https://doi.org/10.1109/ICISC.2018.8398990

  97. Chandra H, Satu S, Mazumder A (2017) Performance analysis of different classification algorithms that predict heart disease severity in Bangladesh. Int J Comput Sci Inf Secur 332–340

  98. Yadav DP, Saini P, Mittal P (2021) Feature optimization based heart disease prediction using machine learning. In: 2021 5th international conference on information systems and computer networks (ISCON), pp 1–5. https://doi.org/10.1109/ISCON52037.2021.9702410

  99. Li JP, Haq AU, Din SU, Khan J, Khan A, Saboor A (2020) Heart disease identification method using machine learning classification in e-healthcare. IEEE Access 8:107562–107582. https://doi.org/10.1109/ACCESS.2020.3001149

  100. Tabassum T, Islam M (2016) An approach of cardiac disease prediction by analyzing ECG signal. In: 2016 3rd international conference on electrical engineering and information communication technology (ICEEICT), pp 1–5

  101. Shen Y, Yang Y, Parish P, Chen Z, Clarke R J, Clifton D A (2016) Risk prediction for cardiovascular disease using ECG data in the China kadoorie biobank. In: 2016 38th annual international conference of the ieee engineering in medicine and biology society (EMBC), pp 2419–2422. https://doi.org/10.1109/embc.2016.7591218

  102. Otoom AF, Abdallah EE, Kilani Y, Kefaye A, Ashour M (2015) Effective diagnosis and monitoring of heart disease. Int J Softw Eng Appl 9:143–156. https://doi.org/10.14257/ijseia.2015.9.1.12

  103. Hossain AI, Sikder S, Das A, Dey A (2021) Applying machine learning classifiers on ECG dataset for predicting heart disease. In: 2021 international conference on automation, control and mechatronics for industry 4.0 (ACMI), pp 1–6. https://doi.org/10.1109/ACMI53878.2021.9528169

  104. Phasinam K, Mondal T, Novaliendry D, Yang CH, Dutta C, Shabaz M (2022) Analyzing the performance of machine learning techniques in disease prediction. J Food Q. https://doi.org/10.1155/2022/7529472

  105. Manjunathan N, Girirajan S, Jaganathan D (2022) Cardiovascular disease prediction using enhanced support vector machine algorithm. In: 2022 6th international conference on computing methodologies and communication (ICCMC), pp 295–302. https://doi.org/10.1109/ICCMC53470.2022.9753916

  106. Boukhatem C, Youssef HY, Nassif AB (2022) Heart disease prediction using machine learning. In: 2022 advances in science and engineering technology international conferences (ASET), pp 1–6. https://doi.org/10.1109/ASET53988.2022.9734880

  107. Gowri J, Kamini R, Vaishnavi G, Thasvin S, Vaishna C (2022) Heart disease prediction using machine learning. Int J Innovat Technol Exploring Eng (IJITEE) 11. https://doi.org/10.35940/ijitee.H9148.0711822

  108. Saboor A, Usman M, Ali S, Samad A, Abrar MF, Ullah N (2022) A method for improving prediction of human heart disease using machine learning algorithms. Mobile Inf Syst 2022:9. https://doi.org/10.1155/2022/1410169

  109. Cp P, Suresh A, Suresh G (2017) Prediction of cardiac arrhythmia type using clustering and regression approach (P-CA-CRA). In: 2017 international conference on advances in computing, communications and informatics (ICACCI), pp 51–54. https://doi.org/10.1109/ICACCI.2017.8125815

  110. Shinde RM, Arjun S, Patil PR, Waghmare J (2015) An intelligent heart disease prediction system using K-Means clustering and Naive Bayes algorithm. Int J Comput Sci Inf Technol 6

  111. Altayeva A, Zharas S, Cho Y I (2016) Medical decision making diagnosis system integrating k-means and Naïve Bayes algorithms. In: 2016 16th international conference on control, automation and systems (ICCAS), pp 1087–1092. https://doi.org/10.1109/ICCAS.2016.7832446

  112. Singh R, Rajesh E (2019) Prediction of heart disease by clustering and classification techniques. Int J Comput Sci Eng. https://doi.org/10.26438/ijcse/v7i5.861866

  113. Thangamani M, Vijayalakshmi R, Ganthimathi M, Ranjitha M,Malarkodi P, Nallusamy D (2020) Efficient classification of heart disease using KMeans clustering algorithm. Int J Eng Trends Technol 68:48–53. https://doi.org/10.14445/22315381/IJETT-V68I12P209

  114. Reddy GD (2021) Heart disease clustering using K-Mean analysis. Int J Adv Res, Ideas Innovations Technol 7

  115. Reddy KVV, Elamvazuthi I, Aziz AA, Paramasivam S, Chua HN (2021) Heart disease risk prediction using machine learning with principal component analysis. In: 2020 8th international conference on intelligent and advanced systems (ICIAS), pp 1–6. https://doi.org/10.1109/ICIAS49414.2021.9642676

  116. Ripan RC, Sarker IH, Hossain SMM, Anwar MM, Nowrozy R, Hoque MM, Furhad H (2021) A data-driven heart disease prediction model through K-Means clustering-based anomaly detection. SN Comput Sci 2:112. https://doi.org/10.1007/s42979-021-00518-7

  117. Nanehkaran YA, Licai Z, Chen J, Jamel AAM et al (2022) Anomaly detection in heart disease using a density-based unsupervised approach. Wireless Commun Mobile Comput 2022. https://doi.org/10.1155/2022/6913043

  118. Dileep P, Rao KN, Bodapati P, Gokuruboyina S, Peddi R, Grover A, Sheetal A (2022) An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-022-07064-0

  119. Satu MS, Tasnim F, Akter T, Halder S (2018) Exploring significant heart disease factors based on semi supervised learning algorithms. In: 2018 international conference on computer, communication, chemical, material and electronic engineering (IC4ME2), pp 1–4. https://doi.org/10.1109/IC4ME2.2018.8465642

  120. Kayikci S (2019) Cardiac sound analyzation using convolutional neural network. In: 2019 3rd international symposium on multidisciplinary studies and innovative technologies (ISMSIT), pp 1–4. https://doi.org/10.1109/ISMSIT.2019.8932952

  121. Ali AA, Hassan HS, Anwar EM (2020) Heart diseases diagnosis based on a novel convolution neural network and gate recurrent unit technique. In: 2020 12th international conference on electrical engineering (ICEENG), pp 145–150. https://doi.org/10.1109/ICEENG45378.2020.9171739

  122. Luo G, Sun G, Wang K, Dong S, Zhang H (2016) A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI. 2016 Computing in cardiology conference (CinC), pp 89–92. https://doi.org/10.22489/CINC.2016.028-224

  123. Luo G, Dong S, Wang K, Zuo W, Cao S, Zhang (2018) Multi-views fusion CNN for left ventricular volumes estimation on cardiac MR images. IEEE Trans Biomed Eng 65:1924–1934. https://doi.org/10.1109/TBME.2017.2762762

  124. Xiong Z, Fedorov VV, Fu X, Cheng E, Macleod R, Zhao J (2019) Fully automatic left atrium segmentation from late gadolinium enhanced magnetic resonance imaging using a dual fully convolutional neural network. IEEE Trans Medical Imaging 38:515–524. https://doi.org/10.1109/tmi.2018.2866845

  125. Tseng LM, Tseng VS (2020) Predicting ventricular fibrillation through deep learning. IEEE Access 8:221886–221896. https://doi.org/10.1109/ACCESS.2020.3042782

  126. Mehmood A, Iqbal M, Mehmood Z, Irtaza A, Nawaz M, Nazir T, Masood MF (2021) Prediction of heart disease using deep convolutional neural networks. Arabian J Sci Eng 1–14. https://doi.org/10.1007/s13369-020-05105-1

  127. Singhal S, Kumar H, Passricha V (2018) Prediction of heart disease using CNN. Am Int J Res Sci Technol Eng Math

  128. Harkulkar N (2020) Heart disease prediction using CNN, deep learning model. Int J Res Appl Sci Eng Technol 8:875–881. https://doi.org/10.22214/ijraset.2020.32671

  129. Hussain SF, Nanda SK, Barigidad S, Akhtar S, Suaib M, Ray NK (2021) Novel deep learning architecture for predicting heart disease using CNN. In: 2021 19th OITS international conference on information technology (OCIT), pp 353–357. https://doi.org/10.1109/OCIT53463.2021.00076

  130. Islam R, Beeravolu A R, Islam MAH, Karim A, Azam S, Akter MS (2021) A performance based study on deep learning algorithms in the efficient prediction of heart disease. In: 2021 2nd international informatics and software engineering conference (IISEC), pp 1–6. https://doi.org/10.1109/IISEC54230.2021.9672415

  131. Arooj S, Rehman SU, Imran A, Almuhaimeed A, Alzahrani AK, Alzahrani A (2022) A deep convolutional neural network for the early detection of heart disease. Biomedicines 2796. https://doi.org/10.3390/biomedicines10112796

  132. Ayon SI, Islam MM, Hossain MR (2020) Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J Res 68:2488–2507. https://doi.org/10.1080/03772063.2020.1713916

  133. Ramprakash P, Sarumathi R, Mowriya R, Nithyavishnupriya S (2020) Heart disease prediction using deep neural network. In: 2020 international conference on inventive computation technologies (ICICT), pp 666–670. https://doi.org/10.1109/ICICT48043.2020.9112443

  134. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande SD, Singh P (2021) Prediction of heart disease using a combination of machine learning and deep learning. Computational Intell Neurosci 2021. https://doi.org/10.1155/2021/8387680

  135. CalderonVilca HD, Callupe KEC, Aliaga RJI, Cuba JB, Cardenas FCM (2019) Early cardiac disease detection using neural networks. In: 2019 7th international engineering, sciences and technology conference (IESTEC), pp 562–567. https://doi.org/10.1109/IESTEC46403.2019.00106

  136. Li H, Luo M, Zheng J, Luo J, Zeng R, Feng N, Du QY, Fang J (2017) An artificial neural network prediction model of congenital heart disease based on risk factors. Medicine 96. https://doi.org/10.1097/md.0000000000006090

  137. Sharma S, Parmar M (2020) Heart diseases prediction using deep learning neural network model. Int J Innovative Technol Exploring Eng (IJITEE) 9

  138. Jayasudha SKB, Sudha PN, Rachana S, Kashyap A, Anusha L (2021) Machine learning techniques for cardiovascular risk score -prediction. In: 2021 IEEE Mysore sub section international conference (MysuruCon), pp 505–509. https://doi.org/10.1109/MysuruCon52639.2021.9641581

  139. Aslam M, Munir MA, Ahmad R, Samiullah M, Hassan N, Shahzadi M, Cui D (2022) Deep neural networks for prediction of cardiovascular diseases. Nano Biomed Eng 14. https://doi.org/10.5101/nbe.v14i1.p81-89

  140. Baviskar V, Verma M, Chatterjee P (2021) A model for heart disease prediction using feature selection with deep learning. Adv Comput 151–168. https://doi.org/10.1007/978-981-16-0401-0_12

  141. Adiba FI, Sharwardy SN, Rahman MZ (2021) Multivariate time series prediction of pediatric ICU data using deep learning. In: 2021 international conference on innovative trends in information technology (ICITIIT), pp 1–6. https://doi.org/10.1109/ICITIIT51526.2021.9399593

  142. Jin B, Che C, Liu Z, Zhang S, Yin X, Wei X (2018) Predicting the risk of heart failure with EHR sequential data modeling. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2789324

  143. Islam MS, Umran HM, Umran SM, Karim MA (2019) Intelligent healthcare platform: cardiovascular disease risk factors prediction using attention module based LSTM. In: 2019 2nd international conference on artificial intelligence and big data (ICAIBD), pp 167–175. https://doi.org/10.1109/ICAIBD.2019.8836998

  144. Kelwade JP, Salankar SS (2016) An optimal structure of multilayer perceptron using particle swarm optimization for the prediction of cardiac arrhythmias. In: 2016 5th international conference on reliability, infocom technologies and optimization (trends and future directions) (ICRITO), pp 426–430. https://doi.org/10.1109/ICRITO.2016.7784993

  145. Subhadra K, Vikas B (2019) Neural network based intelligent system for predicting heart disease. Int J Innovative Technol Exploring Eng (IJITEE) 8:1–4

  146. Banoth R, Godishala AK, Veena R, Yassin H (2022) A healthcare monitoring system for predicting heart disease through recurrent neural network. In: 2022 IEEE 7th international conference for convergence in technology (I2CT), pp 1–7. https://doi.org/10.1109/I2CT54291.2022.9824888

  147. Rubakumar N, Srivatsan BA, Sanjay S, Maheswari VU (2022) Cardiac auscultation classification. In: 2022 8th international conference on advanced computing and communication systems (ICACCS), vol 1, pp 1014–1017. https://doi.org/10.1109/ICACCS54159.2022.9785240

  148. Chauhan U, Kumar V, Chauhan V, Tiwary S, Kumar A(2019) Cardiac arrest prediction using machine learning algorithms. In: 2019 2nd international conference on intelligent computing, instrumentation and control technologies (ICICICT), vol 1, pp 886–890. https://doi.org/10.1109/ICICICT46008.2019.8993296

  149. Mienye ID, Sun Y, Wang Z (2020) Improved sparse autoencoder based artificial neural network approach for prediction of heart disease. Inf Med Unlocked 18:100307. https://doi.org/10.1016/j.imu.2020.100307

  150. Alex PM, Shaji SP (2019) Predictionand diagnosis of heart disease patients using data mining technique. In: 2019 international conference on communication and signal processing (ICCSP), pp 0848–0852. https://doi.org/10.1109/ICCSP.2019.8697977

  151. Sivaranjani R, Yuvaraj N (2019) Artificial intelligence model for earlier prediction of cardiac functionalities using multilayer perceptron. J Phys: Conference Series 1362:012062. https://doi.org/10.1088/1742-6596/1362/1/012062

  152. Gavande S, Chawan P (2022) Prediction of heart disease using neural network. Int Res J Eng Technol (IRJET) 9

  153. Sarra R, Dinar A, Mohammed M (2023) Enhanced accuracy for heart disease prediction using artificial neural network. Indonesian J Electrical Eng Comput Sci 29:375–383. https://doi.org/10.11591/ijeecs.v29.i1.pp375-383

  154. Kelwade JP, Suresh SS (2016) Comparative study of neural networks for prediction of cardiac arrhythmias. IN: 2016 International conference on automatic control and dynamic optimization techniques (ICACDOT), pp 1062–1066. https://doi.org/10.1109/ICACDOT.2016.7877749

  155. Kelwade JP, Suresh SS (2016) Radial basis function neural network for prediction of cardiac arrhythmias based on heart rate time series. In: 2016 IEEE first international conference on control, measurement and instrumentation (CMI), pp 454–458. https://doi.org/10.1109/CMI.2016.7413789

  156. Rajpoot C, Gupta P (2021) A deep learning technique based on generative adversarial network for heart disease prediction. Eng J. https://doi.org/10.4186/ej.year.vol.issue.pp

  157. Li W, Zuo MJ, Zhao H, Xu Q, Chen D (2021) Prediction of coronary heart disease based on combined reinforcement multitask progressive time series networks. Methods 98:96–106. https://doi.org/10.1016/j.ymeth.2021.12.009

  158. Khan MF, Gazara RK, Nofal MM, Chakrabarty S, Dannoun EMA, Hmouz RA, Mursaleen MA (2021) Reinforcing synthetic data for meticulous survival prediction of patients suffering from left ventricular systolic dysfunction. IEEE Access 9:72661–72669. https://doi.org/10.1109/ACCESS.2021.3080617

  159. Malav AP, Kadam KD, Kamat P (2017) Prediction of heart disease using k-means and artificial neural network as hybrid approach to improve accuracy. Int J Eng Technol 9:3081–3085. https://doi.org/10.21817/ijet/2017/v9i4/170904101

  160. Dhar S, Roy K, Dey T, Datta P, Biswas A (2018) A hybrid machine learning approach for prediction of heart diseases. In: 2018 4th international conference on computing communication and automation (ICCCA), pp 1–6. https://doi.org/10.1109/CCAA.2018.8777531

  161. Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542–81554. https://doi.org/10.1109/ACCESS.2019.2923707

  162. Rai HM, Chatterjee K, Mukherjee C (2020) Hybrid CNN-LSTM model for automatic prediction of cardiac arrhythmias from ECG big data. 2020 IEEE 7th Uttar pradesh section international conference on electrical, electronics and computer engineering (UPCON), pp 1–6. https://doi.org/10.1109/UPCON50219.2020.9376450

  163. Elshafie MG, Hagag A, Dahshan ESA, Ismail MA (2021) A hybrid bidirectional LSTM and 1D CNN for heart disease prediction. Int J Netw Secur 135–144. https://doi.org/10.22937/IJCSNS.2021.21.10.18

  164. Angappan K, Meenakshi N, Joel E, Bharanika H, Jothi S (2021) A hybrid approach for heart disease prediction. Proceedings of the first international conference on computing, communication and control system, I3CAC 2021. https://doi.org/10.4108/eai.7-6-2021.2308784

  165. Zhenya Q, Zhang Z (2020) A hybrid cost-sensitive ensemble for heart disease prediction. BMC Med Inf Decision Making 21. https://doi.org/10.21203/rs.2.22946/v1

  166. Khan UJ, Oberoi A, Gill J (2021) Hybrid classification for heart disease prediction using artificial intelligence. In: 2021 5th international conference on computing methodologies and communication (ICCMC), pp 1779–1785. https://doi.org/10.1109/ICCMC51019.2021.9418345

  167. Kibria HB, Matin A (2022) The severity prediction of the binary and multi-class cardiovascular disease - a machine learning-based fusion approach. Comput Biol Chem 98:107672. https://doi.org/10.1016/j.compbiolchem.2022.107672

  168. Gnoguem C, Degila J, Bondiombouy C (2022) Predicting heart disease with multiple classifiers. In: Intelligent vision in healthcare, pp 59–74. https://doi.org/10.1007/978-981-16-7771-7_6

  169. Morya R, Singh S (2022) Prediction model of heart diseases based on hybrid model. J Phys: Conference Series 2327:7. https://doi.org/10.1088/1742-6596/2327/1/012071

  170. Guo S, Zhang H, Gao Y, Wang H, Xu L, Gao Z, Guzzo A, Fortino G (2023) Survival prediction of heart failure patients using motion-based analysis method. Comput Methods Program Biomed 236:107547. https://doi.org/10.1016/j.cmpb.2023.107547

  171. Yekkala I, Dixit S, Jabbar MA (2017) Prediction of heart disease using ensemble learning and particle swarm optimization. In: 2017 international conference on smart technologies for smart nation (SmartTechCon), pp 691–698. https://doi.org/10.1109/SmartTechCon.2017.8358460

  172. Nitten SR, Rachel D, Girija C (2018) Cardiovascular risk prediction based on XGBoost. In: 2018 5th Asia-Pacific world congress on computer science and engineering, pp 246–252. https://doi.org/10.1109/APWConCSE.2018.00047

  173. Atallah R, Mousa AA(2019) Heart disease detection using machine learning majority voting ensemble method. In: 2019 2nd international conference on new trends in computing sciences (ICTCS), pp 1–6. https://doi.org/10.1109/ICTCS.2019.8923053

  174. Latha C, Jeeva S (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Informatics Med Unlocked 20. https://doi.org/10.1016/J.IMU.2019.100203

  175. Mienye ID, Sun Y, Wang Z (2020) An improved ensemble learning approach for the prediction of heart disease risk. Inform Med Unlocked 20:100402. https://doi.org/10.1016/j.imu.2020.100402

  176. Xie J, Wu R, Wang H, Chen H, Xu X, Kong Y, Zhang W (2021) Prediction of cardiovascular diseases using weight learning based on density information. Neurocomputing 452:566–575. https://doi.org/10.1016/j.neucom.2020.10.114

  177. Wenxin X (2020) Heart disease prediction model based on model ensemble. In: 2020 3rd international conference on artificial intelligence and big data (ICAIBD), pp 195–199. https://doi.org/10.1109/ICAIBD49809.2020.9137483

  178. Velusamy D, Ramasamy K (2021) Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Compute Methods Program Biomed 198:105770. https://doi.org/10.1016/j.cmpb.2020.105770

  179. Rahman MJU, Sultan RI, Mahmud F, Shawon A, Khan A (2018) Ensemble of multiple models for robust intelligent heart disease prediction system. In: 2018 4th international conference on electrical engineering and information communication technology (iCEEiCT), pp 58–63. https://doi.org/10.1109/CEEICT.2018.8628152

  180. Kamalapurkar S, Samyama GH (2020) Online portal for prediction of heart disease using machine learning ensemble method (PrHD-ML), pp 1–6. https://doi.org/10.1109/B-HTC50970.2020.9297918

  181. Ahmad GH, Fatima H, Ullah S, Saidi AS, Imdadullah (2022) Efficient medical diagnosis of human heart diseases using machine learning techniques with and without GridSearchCV. IEEE Access 10:80151–80173. https://doi.org/10.1109/ACCESS.2022.3165792

  182. Ye Q, Qiao L, Chen H, Tao Q, Xiao J (2021) Automatic cardiomyopathy diagnosis with a cost-sensitive ensemble classifier. In: 2021 5th Asian conference on artificial intelligence technology (ACAIT), pp 775–779. https://doi.org/10.1109/ACAIT53529.2021.9731304

  183. Kumar KL, Reddy BE (2021) Heart disease detection system using gradient boosting technique. In: 2021 international conference on computing sciences (ICCS), pp 228–233. https://doi.org/10.1109/ICCS54944.2021.00052

  184. Hakim MA, Jahan N, Zerin ZA, Farha AB (2021) Performance evaluation and comparison of ensemble based bagging and boosting machine learning methods for automated early prediction of myocardial infarction. In: 2021 12th international conference on computing communication and networking technologies (ICCCNT), pp 1–6. https://doi.org/10.1109/ICCCNT51525.2021.9580063

  185. Kulshreshth A, Yadav M, Sharma G (2022) Detecting cardiac ailments using machine learning. In: 2022 2nd international conference on intelligent technologies (CONIT), pp 1–5. https://doi.org/10.1109/CONIT55038.2022.9847716

  186. Mamun M, Farjana A, Mamun MA, Ahammed MS, Rahman MM (2022) Heart failure survival prediction using machine learning algorithm: am i safe from heart failure? In: 2022 IEEE World AI IoT Congress (AIIoT), pp 194–200. https://doi.org/10.1109/AIIoT54504.2022.9817303

  187. Shorewala V (2021) Early detection of coronary heart disease using ensemble techniques. Informat Med Unlocked 26:100655. https://doi.org/10.1016/j.imu.2021.100655

  188. Rashme TY, Islam L, Jahan S, Prova AA (2021) Early prediction of cardiovascular diseases using feature selection and machine learning techniques. In: 2021 6th international conference on communication and electronics systems (ICCES), pp 1554–1559. https://doi.org/10.1109/ICCES51350.2021.9489057

  189. Saraf V, Chavan P, Jadhav A (2020) Deep learning challenges in medical imaging. Adv Comput Technolog Appl. https://doi.org/10.1007/978-981-15-3242-9_28

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Acknowledgements

The authors would like to acknowledge the support of Prince Sultan University. Also, this research was funded by Hunan Key Laboratory for Internet of Things in Electricity (Grant No. 2019TP1016) and the National Natural Science Foundation of China (Grant No.72061147004) the National Natural Science Foundation of Hunan Province (Grant No. 2021JJ30055) and the project about research on key technologies of power knowledge graph (Grant No. 5216A6200037).

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  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, People’s Republic of China

    Pierre Claver Bizimana & Zuping Zhang

  2. EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Muhammad Asim, Ahmed A. Abd El-Latif & Mohamed Hammad

  3. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Muhammad Asim

  4. Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El-Koom, 32511, Egypt

    Ahmed A. Abd El-Latif

  5. Information Technology Department, Faculty of Computers and Information, Menoufia University, Shibin El Kom, 32511, Egypt

    Mohamed Hammad

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Bizimana, P.C., Zhang, Z., Asim, M. et al. Learning-based techniques for heart disease prediction: a survey of models and performance metrics. Multimed Tools Appl 83, 39867–39921 (2024). https://doi.org/10.1007/s11042-023-17051-9

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