Machine Learning Algorithms for Heart Disease Prediction
Abstract
Cardiovascular disease, otherwise known as heart disease, encompasses many diseases that affect the heart. Heart disease prediction is among the most complicated tasks in medical field. In the modern age, about one person dies every minute as a result of heart disease. In addition to many factors that contribute to heart disease, it's necessary at this point in time to acquire accurate, reliable, and sensible approaches to make an early diagnosis so that the disease may be managed appropriately. Due to the complexity of finding out the heart condition, the prediction process must be automated to avoid risks related to it and to alert the patient at an early stage. In the healthcare domain, data mining is commonly used to analyze huge, complex medical data and predict heart disease. Researchers apply a variety of data mining and machine learning approaches to analyse huge complex medical data and predict heart disease. In this study, various heart disease attribute are presented, and model is developed on the basis of supervised learning algorithm as K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, SVM, Light GBM and Naïve bayes. This Paper makes use of heart condition dataset available in Kaggle repository. The purpose of this study is to anticipate heart disease risk in patients. The results show that K-nearest neighbor provides the most accurate result.
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References
[2] T.Nagamani, S.Logeswari, B.Gomathy,” Heart Disease Prediction using Data Mining with Mapreduce Algorithm”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-3, January 2019.
[3] Fahd Saleh Alotaibi,” Implementation of Machine Learning Model to predict Heart Failure Disease”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 6, 2019. Asian Journal of Convergence in Technology
ISSN NO: 2350-1146 I.F-5.11
Volume VIII and Issue I
90
[4] Anjan Nikhil Repaka, Sai Deepak Ravikanti, Ramya G Franklin, ”Design And Implementation Heart Disease Prediction Using Naives Bayesian”, International Conference on Trends in Electronics and Information(ICOEI 2019).
[5] UCI, ―Heart Disease Data Set.[Online]. Available (Accessed on May 1 2020): https://www.kaggle.com/ronitf/heart-disease-uci.
[6] Theresa Princy R,J. Thomas,’Human heart Disease Prediction System using Data Mining Techniques’, International Conference on Circuit Power and Computing Technologies,Bangalore,2016.
[7] Nagaraj M Lutimath,Chethan C,Basavaraj S Pol.,’Prediction Of Heart Disease using Machine Learning’, International journal Of Recent Technology and Engineering,8,(2S10), pp 474-477, 2019.
[8] C. B. Rjeily, G. Badr, E. Hassani, A. H., and E. Andres, ―Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field,‖ in Machine Learning Paradigms, 2019, pp. 71–99.
[9] Fajr Ibrahem Alarsan., and Mamoon Younes ‘Analysis and classification of heart diseases using heartbeat features and machine learning algorithms’,Journal Of Big Data,2019;6:81.
[10] Internet source [Online].Available (Accessed on May 1 2021): http://acadpubl.eu/ap.
[11] Jafar Alzubi, Anand Nayyar, Akshi Kumar. "Machine Learning from Theory to Algorithms: An Overview", Journal of Physics: Conference Series, 2018.
[12] Sayali Ambekar, Rashmi Phalnikar,“Disease Risk Prediction by Using Convolutional Neural Network”,2018 Fourth International Conference on Computing Communication Control and Automation.
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