IN HEALTHCARE APPLICATIONS of MACHINE LEARNING ALGORITHMS for PREDICTION of HEART ATTACKS

Year 2022, Issue: 051, 358 - 370, 31.12.2022

Tansu Seslier , Mücella Özbay Karakuş

Abstract

Due to changing lifestyles in the world and in our country, the account of chronic diseases (CD) is rising day after day. CD is one of the most widespread reason of death. About 46% of the death of people in the world, excluding communicable diseases and accidents, are because of cardiovascular diseases (CVDs), according to this study, and 7.4 million of her 17.5 million deaths from these diseases are due to heart attacks. It was something. The number of deaths from cardiovascular disease is estimated to reach 22.2 million in 2030. The fact that most of the agents that are the reasons of the heart disease (HD) that can be prevented and treated is an important phenomenon in reducing cardiovascular disease deaths. Accurate and timely diagnosis of HD is therefore plenty important. Used machine learning (ML) techniques to determine heart attack risk in this study. Therefore, heart attack risk assessment was performed with a less expensive and effective approach. In this study, Logistic Regression, Support Vector Machines (SVM), Nearest Neighbor Algorithms, NaiveBayes, and Random Forest, ML techniques were applied to a data set containing 303 patient records and 14 variables. As a result of the application, the SVM technique achieved the best accuracy outcomes as 87.91%.

Keywords

Machine Learning, Heart Attack Prediction, SVM

Thanks

The authors received no specific grant for the research, authorship, and/or publication of this article. This paper was presented as oral in 2nd International Conference on Engineering and Applied Natural Sciences ICEANS 2022.

References

• [1] Ahmed, M. R., Mahmud, S., Hossin, M., Jahan, H., and Noori, S, (2018), A Cloud Based Four-Tier Architecture for Early Detection of Heart Disease with Machine Learning Algorithms., IEEE 4th International Conference on Computer and Communications, pp. 1951-1955.
• [2] Krishniah, V. V., Sekar, D., and Rao, D., (2012), Predicting the heart attack symptoms using biomedical data mining techniques, The International Journal of Computer Science and Applications, vol. 1, no. 3, pp. 10-18.
• [3] WHO, https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 Accessed on: Jan. 24, 2022
• [4] Taşcı, M. E., and Şamlı, R. (2020), Veri Madenciliği İle Kalp Hastalığı Teşhisi, Avrupa Bilim ve Teknoloji Dergisi, pp. 88-95.
• [5] Caliskan, A., and Yuksel, M., (2017), Classification of coronary artery disease data sets by using a deep neural network. The EuroBiotech Journal, vol:1 no.4, pp. 271-277.
• [6] Soni, J., Ansari, U., Sharma, D., and Soni, S., (2011), Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction, International Journal of Computer Applications, vol. 17, no. 8, pp. 43-48.
• [7] Chandna, D., (2014), Diagnosis of Heart Disease Using Data Mining Algorithm, (IJCSIT) International Journal of Computer Science and Information Technologies, vol. 5, no. 2, pp. 1678-1680.
• [8] Verma, L., Srivastava, S., and Negi, P., (2016), A Hybrid Data MiningModel to Predict Coronary Artery Disease Cases Using Non-Invasive Clinical Data, Journal of Medical Systems, vol. 40, no. 7.
• [9] Pandey, A. K., Pandey, P., Jaiswal, K., and Sen, A. A., (2013), Heart Disease Prediction Model using Decision Tree, IOSR Journal of Computer Engineering (IOSR-JCE), vol. 12, no. 6, pp. 83-86.
• [10] Kim, J., Lee, J., and Lee, Y., (2015), Data-mining-based coronary heart disease risk prediction model using fuzzy logic and decision tree, Healthcare İnformatics Research, vol. 21, no. 3, pp. 167-174.
• [11] Yin, W., Yao, Y., Gu, Y., Bao, W., Cheng H., (2021), Prediction of Heart Disease Probability Based on Various Body Function., pp. 267-275.
• [12] Available:https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset
• [13] Senaviratna, N. A., and Cooray, T., (2019), Diagnosing Multicollinearity of Logistic Regression Model, Asian Journal of Probability and Statistics, vol. 5 no. 2, pp. 1-9.
• [14] Kurt, İ., and Türe, M., (2005), Tıp Öğrencilerinde Alkol Kullanımını Etkileyen Faktörlerin Belirlenmesinde Yapay Sinir Ağları ile Lojistik Regresyon Analizi’nin Karşılaştırılması, Trakya Üiversitesi Tıp Fakültesi Dergisi, vol. 22 no. 3, 142-153.
• [15] Zhou, J., Qiu, Y., Zhu, S., Armaghani, D., Li, C., Nguyen, H., and Yagiz, S., (2021), Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate, Engineering Applications of Artificial Intelligence.
• [16] Widodo, A., and Yang, B.-S., (2007), Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, no. 21, pp. 2560–2574.
• [17] Islam, M., Mahmud, A.A., Machine learning approaches for modeling spammer behavior, Asia information retrieval symposium, pp. 251-260, Springer, Berlin, Heidelberg.
• [18] Sevli, O., (2019), Göğüs Kanseri Teşhisinde Farklı Makine Öğrenmesi Tekniklerinin Performans Karşılaştırması, Avrupa Bilim ve Teknoloji Dergisi no. 16, pp. 176-185.
• [19] Saritas, M. M., and Yasar, A., (2019), Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification, International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 2, pp. 88-91.
• [20] K.Vijiya Kumar., (2019), Random Forest Algorithm for the Prediction of Diabetes, Proceeding of International Conference on Systems Computation Automation and Networking.
• [21] Katarya, R., and Srinivas, P., (2020), Predicting Heart Disease at Early Stages using Machine Learning: A Survey, Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 302-305.
• [22] Kılınç, D., Borandağ, E., Yücalar, F., Tunalı, V., Şimşek, M., and Özçift, A., (2016), KNN Algoritması ve R Dili ile Metin Madenciliği Kullanılarak Bilimsel Makale Tasnifi, Marmara Fen Bilimleri Dergisi no.3, pp. 86-94.
• [23] Saçlı, B., Aydınalp, C., Cansız , G., Joof, S., Yılmaz, T., Çayören, M., Akduman , İ., (2019), Microwave dielectric property based classification of renal calculi: Application of a KNN algorithm, Computers in Biology and Medicine, no.112.
• [24] Uddin, S., Khan, A., Hossain, M., and Moni, M., (2019), Comparing different supervised machine learning algorithms for disease prediction, Mechanical Systems and Signal Processing, vol. 19, no. 281, pp. 1–16.