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C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset

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Abstract

Coronary artery disease (CAD) is one of the most lethal diseases which is major cause of deaths around the globe. CAD is among such diseases with mortality rate approximately 7 million per annum. Though, early detection, prognostication and timely diagnosis can help in mortality rate reduction. Conventional CAD detection systems are cumbersome and expensive. Moreover, scarcity or uneven distribution of radiologists in different geographical locations is a hindrance in early diagnosis. Therefore, this is the time when researchers and doctors are collaboratively looking forward for developing a computational intelligence system in the area of medical imaging systems for prognostication, identification, treatment and disease diagnosis. To support the vision of researchers, a computational intelligence system for coronary artery disease diagnosis, C-CADZ, has been proposed. To validate the model, C-CADZ, the dataset namely, Z-Alizadeh Sani CAD dataset from UCI repository is considered. C-CADZ utilizes the fixed analysis of mixed data (FAMD) for feature extraction. FAMD extracts 96 features. In order to retrieve significant features, nature-inspired algorithms are utilized. C-CADZ implemented Synthetic Minority Oversampling Technique (SMOTE) to handle class-imbalanced data as machine learning (ML) predictive models are built to handle class-balanced datasets. Z-Score normalization technique is used for normalizing the dataset. Furthermore, C-CADZ is trained using ML classifiers, Random Forest (RF) and Extra Trees (ET) and validated using holdout validation scheme with hold-out ratio 3 : 1. Experimentation results show that C-CADZ outperforms state-of-the-art methods of last decades in terms of accuracy. C-CADZ has gained an increase in accuracy from state-of-the-art methods published in 2020 by 5.17% with performance metric 〈Acc, Sens, Spec〉≡〈97.37, 98.15, 95.45〉. The performance analysis shows that achieving highest accuracy and the stable nature of boxplot and ROC-AUC curve of RF-ET makes it suitable for heart disease prediction.

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References

  1. The henry j (2019) kaiser family foundation, snapshots: Comparing projected growth in health care expenditures and the economy. Accessed on : July 25, 2019. Kff.org

  2. World health organization (2019) WHO. Accessed on : July 25, 2019. https://www.who.int

  3. WHO (2019) The top 10 causes of death. Accessed on : July 25, 2019. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

  4. WHO (2019) Global health observatory (GHO) data. Accessed on : July 25, 2019. https://www.who.int/gho/mortality_burden_disease/causes_death/top_10/en/

  5. European coordination committee of the radiological, electromedical, and healthcare it industry: Medical imaging equipment age profile and density. Accessed on : July 25, 2019. http://www.cocir.org/uploads/media/16052_COC_AGE_PROFILE_web_01.pdf

  6. Organisation for economic co-operation and development (OECD): Medical technologies, in health at a glance 2015. Accessed on : July 25, 2019. http://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2015/medical-technologies_health_glance-2015-31-en

  7. Tirkes T, Hollar MA, Tann M, Kohli MD, Akisik F, Sandrasegaran K (2013) Response criteria in oncologic imaging: Review of traditional and new criteria. RadioGraphics 33(5):1323–1341. https://doi.org/10.1148/rg.335125214

    Article  Google Scholar 

  8. Torrisi JM, Schwartz LH, Gollub MJ, Ginsberg MS, Bosl GJ, Hricak H (2011) CT findings of chemotherapy-induced toxicity: What radiologists need to know about the clinical and radiologic manifestations of chemotherapy toxicity. Radiology 258(1):41–56. https://doi.org/10.1148/radiol.10092129

    Article  Google Scholar 

  9. Computer learns to detect skin cancer more accurately than doctors. accessed on : July 25, 2019. https://www.theguardian.com/society/2018/may/29/skin-cancer-computer-learns-to-detect-skin-cancer-more-accurately-than-a-doctor/

  10. Google’s lung cancer detection AI outperforms 6 human radiologists. Accessed on : July 25, 2019. https://venturebeat.com/2019/05/20/googles-lung-cancer-detection-ai-outperforms-6-human-radiologists/

  11. Google AI better than doctors at detecting breast cancer. Accessed on : July 25, 2019. https://www.sciencefocus.com/news/google-ai-better-than-doctors-at-detecting-breast-cancer/

  12. Davie AP, Francis CM, Love MP, Caruana L, Starkey IR, Shaw TRD, Sutherland GR, McMurray JJV (1996) Value of the electrocardiogram in identifying heart failure due to left ventricular systolic dysfunction. BMJ: Bri Med J 312(7025):222–222

    Article  Google Scholar 

  13. Marinho LB, deM.M.Nascimento N, Souza J WM, Gurgel MV, Filho] P PR, [de Albuquerque] V HC (2019) A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification. Fut Gener Comput Syst 97:564–577

    Article  Google Scholar 

  14. Deng S-W, Han J-Q (2016) Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps. Fut Gener Comput Syst 60:13–21

    Article  Google Scholar 

  15. Leung DY, Davidson PM, Cranney GB, Walsh WF (1997) Thromboembolic risks of left atrial thrombus detected by transesophageal echocardiogram. Am J Cardiol 79(5):626–629

    Article  Google Scholar 

  16. Wyman RM, Safian RD, Portway V, Skillman JJ, McKAY RG, Baim DS (1988) Current complications of diagnostic and therapeutic cardiac catheterization. J Am Coll Cardiol 12(6):1400–1406

    Article  Google Scholar 

  17. Rajkumar G, Jayabharathy R, Narasimhan K, Raju N, Easwaran M, Elamaran V, Ramirez-Gonzalez G, Burbano-Fernandez M (2019) Spectral and snr improvement analysis of normal and abnormal heart sound signals using different windows. Fut Gener Comput Syst 92:438–443

    Article  Google Scholar 

  18. Dongdong J, Arunkumar N, Wenyu Z, Beibei L, Xinlei Z, Guangjian Z (2019) Semantic clustering fuzzy C means spectral model based comparative analysis of cardiac color ultrasound and electrocardiogram in patients with left ventricular heart failure and cardiomyopathy. Fut Gener Comput Syst 92:324–328

    Article  Google Scholar 

  19. Brenner DJ, Hall EJ (2007) Computed tomography–an increasing source of radiation exposure. N. Engl. J. Med. 357(22):2277–2284

    Article  Google Scholar 

  20. Nandalur KR, Dwamena BA, Choudhri AF, Nandalur MR, Carlos RC (2007) Diagnostic performance of stress cardiac magnetic resonance imaging in the detection of coronary artery disease: a meta-analysis. J Am Coll Cardiol 50(14):1343–1353

    Article  Google Scholar 

  21. Kuchar DL, Thorburn CW, Sammel NL (1987) Prediction of serious arrhythmic events after myocardial infarction: signal-averaged electrocardiogram, Holter monitoring and radionuclide ventriculography. J Am Coll Cardiol 9(3):531–538

    Article  Google Scholar 

  22. Rajpal S, Alshawabkeh L, Opotowsky AR (2017) Current role of blood and urine biomarkers in the clinical care of adults with congenital heart disease. Cur Cardiol Rep 19(6):50

    Article  Google Scholar 

  23. Alizadehsani R, Hosseini MJ, Khosravi A, Khozeimeh F, Roshanzamir M, Sarrafzadegan N, Nahavandi S (2018) Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries. Comput Methods Program Biomed 162:119–127

    Article  Google Scholar 

  24. Uci machine learning repository, center for machine learning and intelligent systems (2017). Accessed on : Feb 2, 2020. https://archive.ics.uci.edu/ml/machine-learning-databases/00412/

  25. Joloudari JH, Joloudari EH, Saadatfar H, GhasemiGol M, Razavi SM, Mosavi A, Nabipour N, Shamshirband S, Nadai L (2020) Coronary artery disease diagnosis; ranking the significant features using a random trees model. Int J Environ Rese Publ Health 17(3):731

    Article  Google Scholar 

  26. Alizadehsani R, Habibi J, Sani ZA, Mashayekhi H, Boghrati R, Ghandeharioun A, Bahadorian B (2012) Diagnosis of Coronary Artery Disease Using Data mining based on Lab Data and Echo Features. J Med Bioeng 1(1)

  27. Alizadehsani R, Hosseini MJ, Boghrati R, Ghandeharioun A, Khozeimeh F, Sani ZA (2012) Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis. Int J Know Dis Bioinfo (IJKDB) 3(1):59–79

    Google Scholar 

  28. Alizadehsani R, Hosseini MJ, Sani ZA, Ghandeharioun A, Boghrati R (2012) Diagnosis of coronary artery disease using cost-sensitive algorithms. In: IEEE 12th Int. Conf. Data Mining Workshops. IEEE, pp 9–16

  29. Alizadehsani R, Habibi J, Hosseini MJ, Boghrati R, Ghandeharioun A, Bahadorian B, Sani ZA (2012) Diagnosis of coronary artery disease using data mining techniques based on symptoms and ecg features. Eur J Sci Res 82(4):542–553

    Google Scholar 

  30. Alizadehsani R, Habibi J, Sani ZA, Mashayekhi H, Boghrati R, Ghandeharioun A, Khozeimeh F, Alizadeh-Sani F (2013) Diagnosing coronary artery disease via data mining algorithms by considering laboratory and echocardiography features. Res Cardiov Med 2(3):133

    Article  Google Scholar 

  31. Alizadehsani R, Habibi J, Hosseini MJ, Mashayekhi H, Boghrati R, Ghandeharioun A, Bahadorian B, Sani ZA (2013) A data mining approach for diagnosis of coronary artery disease. Comput Methods Programs Biomed 111(1):52–61

    Article  Google Scholar 

  32. Alizadehsani R, Zangooei MH, Hosseini MJ, Habibi J, Khosravi A, Roshanzamir M, Khozeimeh F, Sarrafzadegan N, Nahavandi S (2016) Coronary artery disease detection using computational intelligence methods. Know Based Sys 109:187–197

    Article  Google Scholar 

  33. Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods Program Biomed 141:19–26

    Article  Google Scholar 

  34. Nasarian E, Abdar M, Fahami MA, Alizadehsani R, Hussain S, Basiri ME, Zomorodi-Moghadam M, Zhou X, Pławiak P, Acharya UR et al (2020) Association between work-related features and coronary artery disease: a heterogeneous hybrid feature selection integrated with balancing approach. Pattern Recognition Letters

  35. Abdar M, Ksiazek W, Acharya UR, Tan R-S, Makarenkov V, Plawiak P (2019) A new machine learning technique for an accurate diagnosis of coronary artery disease. Comput Methods Program Biomed 179:104992

    Article  Google Scholar 

  36. Yadav C, Lade S, Suman MK (2014) Predictive analysis for the diagnosis of coronary artery disease using association rule mining. Int J Comp Appl 87(4)

  37. Kolukısa B, Hacılar H, Kuş M, Bakır-Güngör B, Aral A, Güngör VC (2019) Diagnosis of coronary heart disease via classification algorithms and a new feature selection methodology. Int J Data Min Sci 1(1):8–15

    Google Scholar 

  38. Gokulnath CB, Shantharajah SP (2019) An optimized feature selection based on genetic approach and support vector machine for heart disease. Clust Comput 22(6):14777–14787

    Article  Google Scholar 

  39. Jayaraman V, Sultana HP (2019) Artificial gravitational cuckoo search algorithm along with particle bee optimized associative memory neural network for feature selection in heart disease classification. J Ambient Intell Hum Comput:1–10

  40. Harimoorthy K, Thangavelu M (2020) Multi-disease prediction model using improved svm-radial bias technique in healthcare monitoring system. J Ambient Intell Hum Comput:1–9

  41. Sowmiya C, Sumitra P (2020) A hybrid approach for mortality prediction for heart patients using aco-hknn. J Ambient Intell Hum Comput

  42. Selvi RT, Muthulakshmi I (2020) An optimal artificial neural network based big data application for heart disease diagnosis and classification model. J Ambient Intell Hum Comput:1–11

  43. Paul AK, Shill PC, Rabin M RI, Murase K (2018) Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Appl Intell 48(7):1739–1756

    Article  Google Scholar 

  44. Ahmed H, Younis EMG, Hendawi A, Ali AA (2020) Heart disease identification from patients social posts, machine learning solution on spark. Futur Gener Comput Syst 111:714–722

    Article  Google Scholar 

  45. Nilashi M, Ahmadi H, Manaf AA, Rashid TA, Samad S, Shahmoradi L, Aljojo N, Akbari E (2020) Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates. Int J Fuzzy Syste:1–13

  46. Ghiasi MM, Zendehboudi S, Mohsenipour AA (2020) Decision tree based diagnosis of coronary artery disease: Cart model. Comput Methods Programs Biomed 192:105400

    Article  Google Scholar 

  47. Ashish L, Kumar S, Yeligeti S (2021) Ischemic heart disease detection using support vector machine and extreme gradient boosting method. Materials Today: Proceedings

  48. Velusamy D, Ramasamy K (2021) Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset. Comput Methods Prog Biomed 198:105770

    Article  Google Scholar 

  49. Sani Z-A (2019) Z-alizadeh sani data set, uci machine learning repository, center for machine learning and intelligent systems, 2017; Accessed on : July 25, 2019. https://archive.ics.uci.edu/ml/datasets/Z-Alizadeh+Sani

  50. López-Sendón J (2011) The heart failure epidemic. Medicographia 33:363–369

    Google Scholar 

  51. Allen LA et al (2012) Decision making in advanced heart failure. Circulation 125(15):1928–1952

    Article  Google Scholar 

  52. Pagès J (2014) Multiple factor analysis by example using r. CRC Press

  53. Houck CR, Joines J, Kay MG (1995) A genetic algorithm for function optimization: a matlab implementation. Ncsu-ie tr 95(09):1–10

    Google Scholar 

  54. Schmitt LM (2001) Theory of genetic algorithms. Theor Comput Sci 259 (1):1–61. https://doi.org/10.1016/S0304-3975(00)00406-0, http://www.sciencedirect.com/science/article/pii/S0304397500004060

    Article  MathSciNet  MATH  Google Scholar 

  55. El-Maleh AH, Sheikh AT, Sait SM (2013) Binary particle swarm optimization (bpso) based state assignment for area minimization of sequential circuits. Appl Soft Comput 13(12):4832–4840

    Article  Google Scholar 

  56. Rodrigues D, Pereira L AM, Almeida T NS, Papa JP, Souza AN, Ramos C CO, Yang X (2013) Bcs: A binary cuckoo search algorithm for feature selection. In: 2013 IEEE International Symposium on Circuits and Systems (ISCAS), pp 465–468

  57. Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, pp 169–178

  58. Palit S, Sinha SN, Molla MA, Khanra A, Kule M (2011) A cryptanalytic attack on the knapsack cryptosystem using binary firefly algorithm. In: 2011 2nd International conference on computer and communication technology (ICCCT-2011). IEEE, pp 428–432

  59. Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang X-S (2012) Bba: a binary bat algorithm for feature selection. In: 2012 25th SIBGRAPI conference on graphics, Patterns and Images. IEEE, pp 291–297

  60. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) Bgsa: binary gravitational search algorithm. Nat Comput 9(3):727–745

    Article  MathSciNet  MATH  Google Scholar 

  61. Mafarja MM, Eleyan D, Jaber I, Hammouri A, Mirjalili S (2017) Binary dragonfly algorithm for feature selection. In: 2017 International Conference on New Trends in Computing Sciences (ICTCS). IEEE, pp 12–17

  62. Griffin DR, Webster FA, Michael CR (1960) The echolocation of flying insects by bats. Anim Behav 8(3):141–154. https://doi.org/10.1016/0003-3472(60)90022-1

    Article  Google Scholar 

  63. Metzner W (1991) Echolocation behaviour in bats. Sci Prog:453–465

  64. Schnitzler HU, Kalko E KV (2001) Echolocation by Insect Eating Bats: We define four distinct functional groups of bats and find differences in signal structure that correlate with the typical echolocation tasks faced by each group. Bioscience 51(7):557–569. https://doi.org/10.1641/0006-3568(2001)051[0557:EBIEB]2.0.CO;2

    Article  Google Scholar 

  65. Yang X-S (2011) Bat algorithm for multi-objective optimisation. Int J Bio-Inspir Comput 3 (5):267–274

    Article  Google Scholar 

  66. Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: 2009 World congress on nature & biologically inspired computing (NaBIC). IEEE, pp 210–214

  67. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol 4. IEEE, pp 1942–1948

  68. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  MATH  Google Scholar 

  69. Bowyer KW, Chawla NV, Hall LO, Kegelmeyer WP (2011) SMOTE: synthetic minority over-sampling technique. CoRR, arXiv:1106.1813

  70. Han H, Wang W-Y, Mao B-H (2005) Borderline-smote: A new over-sampling method in imbalanced data sets learning. In: Advances in Intelligent Computing. Springer, Berlin, pp 878–887

  71. M. NH, W. CE, Katsuari K (2009) Borderline over-sampling for imbalanced data classification. In: 5th international workshop on computational intelligence & applications ieee smc hiroshima chapter :IWCIA, pp 24–29

  72. Haibo He, Yang Bai, Garcia EA, Shutao Li (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp 1322–1328

  73. Gupta A, Kumar R, Singh Arora H, Raman B (2020) Mifh: A machine intelligence framework for heart disease diagnosis. IEEE Access 8:14659–14674. https://doi.org/10.1109/ACCESS.2019.2962755

    Article  Google Scholar 

  74. Zweig MH, Campbell G (1993) Receiver operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561–577

    Article  Google Scholar 

  75. Qin C-J, Guan Q, Wang X-P (2017) Application of ensemble algorithm integrating multiple criteria feature selection in coronary heart disease detection. Biomed Eng Appl Basis Commun 29(06):1750043

    Article  Google Scholar 

  76. Khan Y, Qamar U, Asad M, Zeb B (2019) Applying feature selection and weight optimization techniques to enhance artificial neural network for heart disease diagnosis. In: Proc. SAI Intelligent Systems Conf. Springer, pp 340–351

  77. Abdar M, Acharya UR, Sarrafzadegan N, Makarenkov V (2019) Ne−nu −svc: A new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access 7:167605–167620

    Article  Google Scholar 

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  1. Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Ankur Gupta, Rahul Kumar & Balasubramanian Raman

  2. Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Harkirat Singh Arora

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Gupta, A., Kumar, R., Arora, H.S. et al. C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset. Appl Intell 52, 2436–2464 (2022). https://doi.org/10.1007/s10489-021-02467-3

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