Heart Attack Analysis and Prediction with Machine Learning Techniques

Year 2024, , 33 - 44, 13.09.2024

Shuaib Jasim , İbrahim Onaran Mustafa Al-asadi

https://doi.org/10.34110/forecasting.1489839

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

Ethical Statement

تم عمل هذا البحث وفق اخلاقيات النشر

Supporting Institution

/

Project Number

1

Thanks

الشكر والتقدير الى الباحثين وتعاونهم الدائم

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