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Intelligent Framework for Prediction of Heart Disease using Deep Learning

  • Research Article-Computer Engineering And Computer Science
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Abstract

Heart diseases pose a serious threat. When arteries that supply oxygen and blood to the heart are completely blocked or narrowed, the cardiac issue happens. The prominent causes of death have been cardiac disease. In a short period, the mortality rate has spiked. Cardiovascular diseases refer to these heart-associated diseases. These diseases are seen more in developing rather than developed countries. Inaccurate diagnosis of the disease may cause fatalities, and hence, precision and safety in diagnosing heart disease would be the prime factor in healthcare practice. In the proposed study, deep learning-based diagnosis system for heart disease prediction is proposed. The proposed classifier model achieves the accuracy for sensitivity with 98.21% the specificity achieving the value of 97.85%, the precision value of 98.41%, recall 97.43%, and 97.09% of accuracy. The BP-NN with mRmR feature extraction obtained a high accuracy rate when compared with the BP-NN classifier without a feature selection process. From the above-obtained results, mRmR with BP-NN algorithm obtains better result compared to the existing algorithms.

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References

  1. Rao, R.: Survey on prediction of heart morbidity using data mining techniques. Knowl. Manag. 1(3), 14–34 (2011)

    Google Scholar 

  2. Davari Dolatabadi, A.; Khadem, S.E.Z.; Asl, B.M.: Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM, Comput. Method. Programs Biomed. 138, 117–126 (2017).

  3. Bui, A.L.; Horwich, T.B.; Fonarow, G.C.: Epidemiology and risk profile of heart failure. Nat. Rev. Cardiol. 8(1), 30–41 (2011)

    Article  Google Scholar 

  4. Heidenreich, P.A.; Trogdon, J.G.; Khavjou, O.A., et al.: Forecasting the future of cardiovascular disease in the United States: a policy statement from the American heart association. Circulation 123(8), 933–944 (2011)

    Article  Google Scholar 

  5. Ghwanmeh, S.; Mohammad, A.; Al-Ibrahim, A.: Innovative artificial neural networks-based decision support system for heart diseases diagnosis. J. Intell. Learn. Syst. Appl. 5(3), 176–183 (2013)

    Google Scholar 

  6. Al-Shayea, Q.K.: Artificial neural networks in medical diagnosis. Int. J. Comput. Sci. Issue. 8(2), 150–154 (2011)

    Google Scholar 

  7. Vanisree, K.; Singaraju, J.: Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks. Int. J. Comput. Appl. 19(6), 6–12 (2011)

    Google Scholar 

  8. Samuel, O.W.; Asogbon, G.M.; Sangaiah, A.K.; Fang, P.; Li, G.: An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction. Expert Syst. Appl. 68, 163–172 (2017)

    Article  Google Scholar 

  9. Gokulnath, C.B.; Shantharajah, S.P.: An optimized feature selection based on genetic approach and support vector machine for heart disease. Clust. Comput. 22(6), 14777–14787 (2019)

    Article  Google Scholar 

  10. Olaniyi, E.O.; Oyedotun, O.K.: Heart diseases diagnosis using neural networks arbitration. Int. J. Intel. Syst. Appl. 7(12), 75–82 (2015)

    Google Scholar 

  11. Das, R.; Turkoglu, I.; Sengur, A.: Effective diagnosis of heart disease through neural networks ensembles. Expert Syst. Appl. 36(4), 7675–7680 (2009)

    Article  Google Scholar 

  12. Jabbar, M.A.; Deekshatulu, B.L.; Chandra, P.: Classification of heart disease using artificial neural network and feature subset selection, Glob. J. Comput. Sci. Technol. Neural Artif. Intel. 13(11) (2013)

  13. Peng, H.; Long, F.; Ding, C.: Feature selection based on mutual information criteria of max-dependency, maxrelevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intel. 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  14. Chen, H.-L.; Yang, B.; Liu, J.; Liu, D.Y.: A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl. 38(7), 9014–9022 (2011)

    Article  Google Scholar 

  15. Babu, G.C.; Shantharajah, S.P.: Optimal body mass index cutoff point for cardiovascular disease and high blood pressure. Neural Comput. Appl. 31(5), 1585–1594 (2019)

    Article  Google Scholar 

  16. Patil, S.B.; Kumaraswamy, Y.S.: Extraction of significant patterns from heart disease warehouses for heart attack prediction, IJCSNS Int. J. Comput. Sci. Netw. 228 Secur., 9(2) (2009)

  17. Abdul, S.; Bhagile, V.D.; Manza, R.R.; Ramteke, R.J.: Diagnosis and medical prescription of heart disease using support vector machine and feed forward back propagation technique, (IJCSE) Int. J. Comput. Sci. Eng. 02(06), 2150–2159 (2010)

  18. Thuy Nguyen, T.T.; Davis, D.N.: A clustering algorithm for predicting cardio vascular risk. In: Proceedings of the World Congress on Engineering 2017 Vol IWCE 2007, London, U.K (2017)

  19. Hu, G.; Root, M.M.: Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings, Europ. Sci. Cardiol. (2015)

  20. Kumar, P.M.; Lokesh, S.; Varatharajan, R.; Babu, G.C.; Parthasarathy, P.: Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Futur. Gener. Comput. Syst. 86, 527–534 (2018)

    Article  Google Scholar 

  21. Brahmi, B.; Shirvani, M.H.: Prediction and diagnosis of heart disease by data mining techniques. J. Multidiscip. Eng. Sci. Technol. 2(2), 164–168 (2015)

    Google Scholar 

  22. Ajam, N.: Heart disease diagnoses using artificial neural network. Int. Insit. Sci. Technol. Educ. 5(4), 7–11 (2015)

    Google Scholar 

  23. Mujawar, S.H.; Devale, P.R.: Prediction of heart disease using modified K-means and by using Naïve bayes. Int. J. Innov. Res. Comput. Commun. Eng. 3, 10265–10273 (2015)

    Google Scholar 

  24. Gaziano, T.A.; Bitton, A.; Anand, S.; Abrahams-Gessel, S.; Murphy, A.: Growing epidemic of coronary heart disease in low-and middle-income countries. Curr. Probl. Cardiol. 35(2), 72–115 (2010)

    Article  Google Scholar 

  25. Kumar, P.M., Hong, C.S., Babu, G.C., Selvaraj, J., Gandhi, U.D.: Cloud-and IoT-based deep learning technique-incorporated secured health monitoring system for dead diseases. Soft Comput. 1–16 (2021)

  26. Pouriyeh, S.; Vahid, S.; Sannino, G.; De Pietro, G.; Arabnia, H.; Gutierrez, J.A.: Comprehensive investigation and comparison of machine learning techniques in the domain of heart disease. In: 2017 IEEE Symposium on Computers and Communications (ISCC). IEEE. p. 204–207

  27. Otoom, A.F.; Abdallah, E.E.; Kilani, Y.; Kefaye, A.; Ashour, M.: Effective diagnosis and monitoring of heart disease. Int. J. Softw. Eng. Appl. 9(1), 143–156 (2015)

    Google Scholar 

  28. Duff, F.L.; Munteanb, C.; Cuggiaa, M.; Mabob, P.: Predicting survival causes after out of hospital cardiac arrest using data mining method. Stud. Health Technol. Inform. 107(Pt. 2), 1256–1259 (2004)

    Google Scholar 

  29. Szymanski, B.; Han, L.; Embrechts, M.; Ross, A.; Sternickel, K.; Zhu, L.: Using efficient Supanova Kernel for heart disease diagnosis. In: Procedings of ANNIE 2006, intelligent engineering systems through artificial neural networks 16, 305–310 (2006)

  30. Noh, K.; Lee, H.G.; Shon, H.-S.; Lee, B.J.; Ryu, K.H.: Associative Classification Approach for Diagnosing Cardiovascular Disease, vol. 345, pp. 721–727. Springer (2006)

  31. Dangare, C.S.; Apte, S.S.: Improved study of heart disease prediction system using data mining classification techniques. Int. J. Comput. Appl. 47(10), 44–48 (2012)

    Google Scholar 

  32. Fang, X.; Hodge, B.M.; Du, E.; Zhang, N.; Li, F.: Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: a sparse correlation matrix approach. Appl. Energy 230, 531–539 (2018)

    Article  Google Scholar 

  33. Gavhane, A.; Kokkula, G.; Pandya, I.; Devadkar, K.: Prediction of heart disease using machine learning. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1275–1278. IEEE (2018)

  34. Jenzi, I.; Priyanka, P.; Alli, P.: A reliable classifier model using data mining approach for heart disease prediction. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(3), 20–24 (2013)

    Google Scholar 

  35. Lopez, A.D.; Mathers, C.D.; Ezzati, M.; Jamison, D.T.; Murray, C.J.: Global and regional burden of disease and risk factors, 2001: systematic analysis of population health hdata. Lancet 367(9524), 1747–1757 (2016)

    Article  Google Scholar 

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the General Research Project under grant number (R.G.P.1/200/41).

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Authors and Affiliations

  1. Computer Science and Engineering, Shreenivasa Engineering College, Dharmapuri, India

    Sofia Mary Vincent Paul

  2. Department of Computer Science and Engineering, Sona College of Technology, Salem, India

    Sathiyabhama Balasubramaniam

  3. Sri Shakthi Institute of Engineering and Technology, Coimbatore, India

    Parthasarathy Panchatcharam

  4. Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do, South Korea

    Priyan Malarvizhi Kumar

  5. College of Computer Science and Center for Artificial Intelligence (CAI), King Khalid University, Abha, Kingdom of Saudi Arabia

    Azath Mubarakali

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  1. Sofia Mary Vincent Paul
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  2. Sathiyabhama Balasubramaniam
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Correspondence to Priyan Malarvizhi Kumar.

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Vincent Paul, S.M., Balasubramaniam, S., Panchatcharam, P. et al. Intelligent Framework for Prediction of Heart Disease using Deep Learning. Arab J Sci Eng 47, 2159–2169 (2022). https://doi.org/10.1007/s13369-021-06058-9

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  • DOI: https://doi.org/10.1007/s13369-021-06058-9

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