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Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 2/2022

13-09-2021 | Research Article-Computer Engineering And Computer Science

Intelligent Framework for Prediction of Heart Disease using Deep Learning

Authors: Sofia Mary Vincent Paul, Sathiyabhama Balasubramaniam, Parthasarathy Panchatcharam, Priyan Malarvizhi Kumar, Azath Mubarakali

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

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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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Literature
1.
Rao, R.: Survey on prediction of heart morbidity using data mining techniques. Knowl. Manag. 1(3), 14–34 (2011)
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)CrossRef
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)CrossRef
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)
6.
Al-Shayea, Q.K.: Artificial neural networks in medical diagnosis. Int. J. Comput. Sci. Issue. 8(2), 150–154 (2011)
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)
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)CrossRef
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)CrossRef
10.
Olaniyi, E.O.; Oyedotun, O.K.: Heart diseases diagnosis using neural networks arbitration. Int. J. Intel. Syst. Appl. 7(12), 75–82 (2015)
11.
Das, R.; Turkoglu, I.; Sengur, A.: Effective diagnosis of heart disease through neural networks ensembles. Expert Syst. Appl. 36(4), 7675–7680 (2009)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)
22.
Ajam, N.: Heart disease diagnoses using artificial neural network. Int. Insit. Sci. Technol. Educ. 5(4), 7–11 (2015)
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)
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)CrossRef
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)
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)
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)
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)CrossRef
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)
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)CrossRef
Metadata
Title
Intelligent Framework for Prediction of Heart Disease using Deep Learning
Authors
Sofia Mary Vincent Paul
Sathiyabhama Balasubramaniam
Parthasarathy Panchatcharam
Priyan Malarvizhi Kumar
Azath Mubarakali
Publication date
13-09-2021
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06058-9

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