Effective Models for Predicting Heart Disease Using Machine Learning Techniques – A Comparative Study

Authors

D. Trabay, Information System Department, Obour Institutes for Obour High Institute for Management and computers and Information Systems, Kilo 21 Cairo / Belbeis, Obour City, EgyptFollow
W. Gharibi, Division of Computing, Analytics, and Mathematics, School of Computing and Engineering, University of Missouri- Kansas City (UMKC), USAFollow
W. M. Abd-Elhafiez, Computer Science Department, Faculty of Computers and Artificial Intelligence, Sohag University, Sohag, Egypt\\ College of Computer Science & Information Technology, Jazan University, Jazan, Kingdom of Saudi ArabiaFollow

Abstract

Cardiovascular disease is one of the most important causes of death in the modern world, and a significant barrier to clinical information assessment may be the expectation of cardiovascular illness. Machine learning (ML) has proven helpful for forecasting and decision-making in the healthcare industrys large amount of data. Moreover, ML algorithms have been applied in many important fields such as the internet of things and others. In this paper, we applied various ML methods to predict and classify heart patients disease including K-Nearest Neighbor Algorithm (KNN), Naïve Bayes (NB), Neural Network (NN), Decision trees (DT), Support Vector Machine (SVM), Random-Forest (RF), Logistic Regression (LR), Gradient-Boosting GB), Stochastic Gradient Descent (SGD), and Ada-Boost. All models were evaluated, and the most accurate predictive model was chosen to increase the accuracy of heart attack prediction. Compared to other models, our results are efficient, and adequate and could help to predict heart disease more effectively and precisely.

Recommended Citation

Trabay, D.; Gharibi, W.; and M. Abd-Elhafiez, W. (2023) "Effective Models for Predicting Heart Disease Using Machine Learning Techniques – A Comparative Study," Information Sciences Letters: Vol. 12 : Iss. 5 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol12/iss5/5