Optimization of Supervised Learning Algorithms for Early Prediction of Heart Attack Risk

Angge Firizkiansah; Imron Rizki Maulana; Ali Muhammad; Aliyah Kurniasih

doi:10.59934/jaiea.v4i3.1020

Authors

Angge Firizkiansah Universitas Sains Indonesia
Imron Rizki Maulana Universitas Sains Indonesia
Ali Muhammad Universitas Sains Indonesia
Aliyah Kurniasih Universitas Ary Ginanjar

DOI:

https://doi.org/10.59934/jaiea.v4i3.1020

Keywords:

Early Prediction; Heart Attack Risk; Optimization; Random Forest, Supervised Learning

Abstract

Cardiovascular disease, particularly heart attacks, persists as a primary global cause of mortality. Heart attacks arise from an abrupt obstruction of oxygenated blood flow to a segment of the cardiac muscle, resulting in inadequate oxygen supply to the heart. This obstruction may stem from modifiable risk factors, including suboptimal dietary habits, physical inactivity, obesity, and tobacco consumption, alongside non-modifiable factors such as age, sex, and familial predisposition. Contemporary research increasingly focuses on preemptive strategies against heart attacks to mitigate associated mortality rates. One such strategy involves the application of artificial intelligence for predictive modeling of heart attack risk. These models may utilize machine learning algorithms, such as logistic regression, support vector machine, k-nearest neighbors, and random forest, all categorized under supervised learning paradigms. This study undertakes a thorough examination and optimization of diverse supervised learning algorithms for the prospective prediction of heart attack risk. Findings suggest that machine learning algorithms possess utility in predicting heart attack risk, with the random forest model demonstrating a peak accuracy of 64%. Nevertheless, the model's efficacy is constrained by high feature dimensionality, suggesting avenues for refinement via feature dimension reduction techniques and meticulous hyperparameter optimization across the employed machine learning algorithms.

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