Abstract
Coronary Artery Disease (CAD) is one of the leading causes of death in humans across the world over the last few decades. Coronary artery disease also leads to disability, decreased quality of life and serious illness. CAD can be controlled by identifying the risk factors and on timely diagnosis can also help to reduce the cause of heart failure (death). The conventional method of going through medical history proved that they were less effective in early identification of the disease. So, modern and emerging methods like AI, Machine Learning are more reliable and effective in identifying people with heart disease and can help in containing mortality rate. In the proposed work, three machine learning classification methods (Random Forest, XGBoost and Neural network) are auto-tuned using genetic algorithms to find the most prominent features for maximizing classification performance. These methods are applied to the Z-Alizadeh Sani dataset having demographic examination, ECG, Laboratory and echo data of 303 patients. The computational results of the above application show that three approaches need further ensemble by giving equal importance to all three models to increase the overall performance in assessing the risk and forecasting the presence of disease in the 303 patients.
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References
Yekkala, I., Dixit, S., Jabbar, M.A.: Prediction of heart disease using ensemble learning and Particle Swarm Optimization, 691–698 (2018). https://doi.org/10.1109/SmartTechCon.2017.8358460
Yekkala, I., Dixit, S.: Prediction of heart disease using random forest and rough set based feature selection 3, 1–12. https://doi.org/10.4018/ijbdah.2018010101
Deshmukh, N., Dixit, S., Khondanpur, B.I.: Evaluation of heart rate using reflectance of an image. In: Advanced Intelligent Systems and Computing, vol. 516, pp. 571–578. https://doi.org/10.1007/978-981-10-3156-4_60
Ministry of Health of the Republic of Indonesia (n.d.). http://www.depkes.go.id/article/view/201410080002/lingkungan-sehat-jantung-sehat.html. Accessed 25 Aug 2019
Nashif, S., Raihan, M.R., Islam, M.R., Imam, M.H.: Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system (06), 854–873 (2018). https://doi.org/10.4236/wjet.2018.64057
Cardiovascular diseases (CVDs) (n.d.). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 25 Aug 2019
Sanchis-Gomar, F., Perez-Quilis, C., Leischik, R., Lucia, A.: Epidemiology of coronary heart disease and acute coronary syndrome 4, 1–12 (2016). https://doi.org/10.21037/atm.2016.06.33
Durairaj, M., Ramasamy, N.: A comparison of the perceptive approaches for preprocessing the data set for predicting fertility success rate 9, 255–260 (2016)
Nashif, S., Raihan, M.R., Islam, M.R., Imam, M.H.: Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system 06, 854–873 (2018). https://doi.org/10.4236/wjet.2018.64057
Amin, M.S., Chiam, Y.K., Varathan, K.D.: Identification of significant features and data mining techniques in predicting heart disease 36, 82–93 (2019). https://doi.org/10.1016/j.tele.2018.11.007
Vanisree, K., Singaraju, J.: Decision support system for congenital heart disease diagnosis based on signs and symptoms using neural networks 19, 6–12 (2011). https://doi.org/10.5120/2368-3115
Boughaci, D., Alkhawaldeh, A.A.: Three local search-based methods for feature selection in credit scoring. Vietnam J. Comput. Sci. 5(2), 107–121 (2018). https://doi.org/10.1007/s40595-018-0107-y
Sheikhpour, R., Sarram, M.A., Gharaghani, S., Chahooki, M.A.Z.: A Survey on semi-supervised feature selection methods 64, 141–158 (2017). https://doi.org/10.1016/j.patcog.2016.11.003
Khourdifi, Y., Bahaj, M.: Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization 12, 242–252 (2019). https://doi.org/10.22266/ijies2019.0228.24
Di Noia, A., Martino, A., Montanari, P., Rizzi, A.: Supervised machine learning techniques and genetic optimization for occupational diseases risk prediction. Soft. Comput. 24(6), 4393–4406 (2019). https://doi.org/10.1007/s00500-019-04200-2
Ilayaraja, M., Meyyappan, T.: Efficient data mining method to predict the risk of heart diseases through frequent itemsets 70, 586–592 (2015). https://doi.org/10.1016/j.procs.2015.10.040
Latha, C.B.C., Jeeva, S.C.: Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques 16, 100203 (2019). https://doi.org/10.1016/j.imu.2019.100203
Purushottam, Saxena, K., Sharma, R.: Efficient heart disease prediction system 85, 962–969 (2016). https://doi.org/10.1016/j.procs.2016.05.288
Alizadehsani, R., Hosseini, M.J., Khosravi, A., Khozeimeh, F., Roshanzamir, M., Sarrafzadegan, N., et al.: Based on the stenosis prediction of separate coronary arteries 2018;162:119–27. https://doi.org/10.1016/j.cmpb.2018.05.009
Srinivas, K., Rani, B.K., Govrdhan, A.: Applications of data mining techniques in healthcare and prediction of heart attacks 7, 172–176 (2018). https://doi.org/10.20894/ijdmta.102.007.001.027
De Vos, A., Soens, N., Vaiman, V., Vance, C.M.: Predictive data mining for medical diagnosis: an overview of heart disease prediction. Int. J. Comput. Appl. 17, 119–138 (2008)
Kara, S., Dirgenali, F.: A system to diagnose atherosclerosis via wavelet transforms, principal component analysis and artificial neural networks 32, 632–640 (2007). https://doi.org/10.1016/j.eswa.2006.01.043
Atiya, A.F., Al-Ani, A.: A penalized likelihood based pattern classification algorithm 42, 2684–2694 (2009). https://doi.org/10.1016/j.patcog.2009.04.016
Tsipouras, M.G., Exarchos, T.P., Fotiadis, D.I., Kotsia, A.P., Vakalis, K.V., Naka, K.K., et al.: Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling 12, 447–458 (2008). https://doi.org/10.1109/TITB.2007.907985
Lee, H.G., Noh, K.Y., Ryu, K.H.: A data mining approach for coronary heart disease prediction using HRV features and carotid arterial wall thickness 1, 200–206 (2008). https://doi.org/10.1109/BMEI.2008.189
Zheng, G., Jiang, M., He, X., Zhao, J., Guo, H., Chen, G., et al.: Discrete derivative: a data slicing algorithm for exploration of sharing biological networks between rheumatoid arthritis and coronary heart disease 4, 1–21 (2011). https://doi.org/10.1186/1756-0381-4-18
Noreen, K., Azween, A., Belhaouari, S.B., Sellapan, P., Saeed, A.B., Nilanjan, D.: Ensemble clustering algorithm with supervised classification of clinical data for early diagnosis of coronary artery disease 6, 78–87 (2016). https://doi.org/10.1166/jmihi.2016.1593
Alizadehsani, R., Habibi, J., Hosseini, M.J., Mashayekhi, H., Boghrati, R., Ghandeharioun, A., Bahadorian, B., Sani, Z.A.: A data mining approach for diagnosis of coronary artery disease 111, 52–61 (2013). https://doi.org/10.1016/j.cmpb.2013.03.004
Thomas, J., Princy, R.T.: Human heart disease prediction system using data mining techniques. In: 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT) (2016). https://doi.org/10.1109/iccpct.7530265
Saxena, R., Johri, A., Deep, V., Sharma, P.: Heart diseases prediction system using CHC-TSS Evolutionary, KNN, and decision tree classification algorithm. In: Emerging Technologies in Data Mining and Information Security, pp. 809–819 (2019). https://doi.org/10.1007/978-981-13-1498-8_71
Kim, J., Lee, J., Lee, Y.: Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree. Healthc. Inform. Res. 21(3), 167 (2015). https://doi.org/10.4258/hir.2015.21.3.167
Maji, S., Arora, S.: Decision tree algorithms for prediction of heart disease. In: Lecture Notes in Networks and Systems, pp. 447–454 (2019). https://doi.org/10.1007/978-981-13-0586-3_45
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Yekkala, I., Dixit, S. (2021). A Novel Approach for Heart Disease Prediction Using Genetic Algorithm and Ensemble Classification. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_36
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