Heart Attack Analysis and Prediction with Machine Learning Techniques

Authors : Shuaib Jasim, İbrahim Onaran, Mustafa Alasadi

Pages : 33-44

Doi:10.34110/forecasting.1489839

View : 21 | Download : 27

Publication Date : 2024-09-13

Article Type : Research Paper

Abstract :This study explores the use of machine learning algorithms to analyze and predict heart attacks, focusing on genetics, lifestyle, medical history, and biometric factors. The data was analyzed using logistic regression, support vector machines, decision trees, and random forests. Support vector machines were found to be the most effective model for predicting heart attack risk, with a high accuracy rate and low error rate. The study highlights the potential of machine learning in assisting healthcare professionals and individuals in determining heart attack risk and taking preventive measures.
Keywords : التعلم الالي, النوبة القلبية, البايثون