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An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm

  • S.I. : Neuro, fuzzy and their Hybridization
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Abstract

Heart disease involves many diseases like block blood vessels, heart attack, chest pain or stroke. Heart disease will affect the muscles, valves or heart rate, and bypass surgery or coronary artery surgery will be used to treat these problems. In this paper, UCI heart disease dataset and real time dataset are used to test the deep learning techniques which are compared with the traditional methods. To improve the accuracy of the traditional methods, cluster-based bi-directional long-short term memory (C-BiLSTM) has been proposed. The UCI and real time heart disease dataset are used for experimental results, and both the datasets are used as inputs through the K-Means clustering algorithm for the removal of duplicate data, and then, the heart disease has been predicted using C-BiLSTM approach. The conventional classifier methods such as Regression Tree, SVM, Logistic Regression, KNN, Gated Recurrent Unit and Ensemble are compared with C-BiLSTM, and the efficiency of the system is demonstrated in terms of accuracy, sensitivity and F1 score. The results show that the C-BiLSTM proves to be the best with 94.78% accuracy of UCI dataset and 92.84% of real time dataset compared to the six conventional methods for providing better prediction of heart disease.

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References

  1. Mozaffarian D, Wilson PW, Kannel WB (2008) Beyond established and novel risk factors: lifestyle risk factors for cardiovascular disease. Circulation 117(23):3031–3038

    Article  Google Scholar 

  2. Poirier P (2008) Healthy lifestyle: even if you are doing everything right, extra weight carries an excess risk of acute coronary events. Circulation 117:3057–3059

    Article  Google Scholar 

  3. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press, Cambridge

    MATH  Google Scholar 

  4. Kanimozhi VA, Karthikeyan T (2016) A survey on machine learning algorithms in data mining for prediction of heart disease. Int J Adv Res Compu Commun Eng 5(4):552–557

    Google Scholar 

  5. Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI (2017) Heart failure: diagnosis, severity estimation and prediction of adverse events through machine learning techniques. Comput Struct Biotechnol J 15:26–47

    Article  Google Scholar 

  6. Anbarasi M, Anupriya E, Iyengar NCHSN (2010) Enhanced prediction of heart disease with feature subset selection using genetic algorithm. Int J Eng Sci Technol 2(10):5370–5376

    Google Scholar 

  7. Florence S, BhuvaneswariAmma NG, Annapoorani G, Malathi K (2014) Predicting the risk of heart attacks using neural network and decision tree. Int J Innov Res Comput Commun Eng 2(11):2320–9798

    Google Scholar 

  8. HD Masethe, MA Masethe (2014) Prediction of heart disease using classification algorithms. In: Proceedings of the world congress on engineering and computer Science 2:22-24

  9. Zhou P, Liu C, Liu Q, Dai L, Jiang H, (2013) A cluster-based multiple deep neural networks method for large vocabulary continuous speech recognition. In: IEEE international conference on acoustics, speech and signal processing, pp. 6650–6654

  10. Lee J-G, Jun S, Young-Won Cho MS, Lee H, Kim GB, JoonBeomSeo MD, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570–584

    Article  Google Scholar 

  11. Shankar V, Kumar V, Devagade U (2020) Heart disease prediction using CNN algorithm. SN COMPUT SCI 1:170. https://doi.org/10.1007/s42979-020-0097-6

    Article  Google Scholar 

  12. Karthikeyan T, Kanimozhi VA (2017) Deep learning approach for prediction of heart disease using data mining classification algorithm deep belief network. Int J Adv Res Sci Eng Technol 4(1):3194–3201

    Google Scholar 

  13. Dileep P, Rao KN (2019) An efficient feature selection based heart disease prediction model. Int J Adv Sci Technol 28(9):309–323

    Google Scholar 

  14. Krishnan S, Magalingam P, Ibrahim R (2021) Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction. Int J Electr Comput Eng 11(6):5467–5476

    Google Scholar 

  15. Nguyen DK, Lan CH, Chan CL (2021) Deep ensemble learning approaches in healthcare to enhance the prediction and diagnosing performance: the workflows, deployments, and surveys on the statistical, image-based, and sequential datasets. Int J Environ Res Public Health 18(20):10811. https://doi.org/10.3390/ijerph182010811

    Article  Google Scholar 

  16. Baccouche A, Garcia-Zapirain B, Castillo Olea C, Elmaghraby A (2020) Ensemble deep learning models for heart disease classification: a case study from Mexico. Information 11:207. https://doi.org/10.3390/info11040207

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Zhang D, Chen Y, Chen Y, Ye S, Cai W, Jiang J, Xu Y, Zheng G, Chen M (2021) Heart disease prediction based on the embedded feature selection method and deep neural network. J Healthc Eng 2021:6260022. https://doi.org/10.1155/2021/6260022

    Article  Google Scholar 

  19. Krishnan S, Magalingam P, Ibrahim RB (2020) Advanced recurrent neural network with tensor flow for heart disease prediction. Int J Adv Sci Technol 29(5):966–977

    Google Scholar 

  20. Javid I, Alsaedi AKZ, Ghazali R (2020) Enhanced accuracy of heart disease prediction using machine learning and recurrent neural networks ensemble majority voting method. Int J Adv Comput Sci Appl (IJACSA) 11(3):540–551. https://doi.org/10.14569/ijacsa.2020.0110369

    Article  Google Scholar 

  21. G Luo, R An, K Wang, S Dong H Zhang (2016) A deep learning network for right ventricle segmentation in short-axis MRI Computing in Cardiology Conference (CinC)

  22. S Dong, G Luo, G Sun, K Wang H Zhang (2016) A combined multi-scale deep learning and random forests approach for direct left ventricular volumes estimation in 3D echocardiography.Computing in Cardiology Conference (CinC)

  23. G Luo, G Sun, K Wang, S Dong H Zhang (2016) A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI Computing in Cardiology Conference (CinC), Vancouver, BC, Canada

  24. Wang J et al (2017) Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans Med Imaging 5:1172–1181. https://doi.org/10.1109/TMI.2017.2655486

    Article  Google Scholar 

  25. Ali L, Rahman A, Khan A, Zhou M, Javeed A, Khan JA (2019) An automated diagnostic system for heart disease prediction based on x2 statistical model and optimally configured deep neural network. IEEE Access 7:34938–34945

    Article  Google Scholar 

  26. Solanki Y, Sharma S (2019) Analysis and prediction of heart health using deep learning approach. Int J Comput Sci Eng 7(8):2347–2693

    Google Scholar 

  27. Nandhini S, Debnath M, Sharma A, Pushkar (2018) Heart disease prediction using machine learning. Int J Recent Eng Res Dev 3(10):39–46

    Google Scholar 

  28. Ramprakash P, Sarumathi R, Mowriya R, Nithyavishnupriya S (2020) Heart disease prediction using deep neural network. Int Conf Invent Comput Technol. https://doi.org/10.1109/ICICT48043.2020.9112443

    Article  Google Scholar 

  29. Sharma S, Parmar M (2020) Heart diseases prediction using deep learning neural network model. Int J Innov Technol Explor Eng 9(3):2244–2248

    Article  Google Scholar 

  30. AN Repaka, SD Ravikanti RG Franklin 2019 Design and implementing heart disease prediction using naives Bayesian. In: 3rd International conference on trends in electronics and informatics (ICOEI), Tirunelveli, India

  31. Newman D, Hettich S, Blake C, Merz C (1998) UCI Repository of machine learning databases. http://www.ics.uci.edu/~mlearn/MLRepository.html

Download references

Acknowledgements

The authors would like to thank the editors and the reviewers for their insightful comments and suggestions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, 500100, India

    P. Dileep

  2. Department of CS and SE, AUCE(A) at Andhra University, Visakhapatnam, Andhra Pradesh, India

    Kunjam Nageswara Rao & Prajna Bodapati

  3. Department of CSE, LENDI Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India

    Sitaratnam Gokuruboyina

  4. Department of CSE, ACE Engineering College, Hyderabad, Telangana, India

    Revathy Peddi

  5. Department of Electronics and Communication Engineering, Shaheed Bhagat Singh State University, Ferozepur, Punjab, India

    Amit Grover

  6. Department of Engineering and Technology, Guru Nanak Dev University, Regional Campus, Gurdaspur, Punjab, India

    Anu Sheetal

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  1. P. Dileep
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Correspondence to P. Dileep.

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Dileep, P., Rao, K.N., Bodapati, P. et al. An automatic heart disease prediction using cluster-based bi-directional LSTM (C-BiLSTM) algorithm. Neural Comput & Applic 35, 7253–7266 (2023). https://doi.org/10.1007/s00521-022-07064-0

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  • DOI: https://doi.org/10.1007/s00521-022-07064-0

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