Performance Comparison of Supervised Learning Algorithms in Heart Disease Risk Classification

Fatimah  Azzahra; Muhammad Rafiq Pohan; Ainul Mardhiah Binti Mohammed Rafiq; Imran Hazim Bin Abdullah Salim; Azwa Nurnisya Binti Ayub; Nuralya Medina Binti Mohammad Nizam

doi:10.57152/predatecs.v4i1.1856

Authors

Fatimah Azzahra Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Muhammad Rafiq Pohan Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Ainul Mardhiah Binti Mohammed Rafiq International Islamic University Malaysia, Malaysia
Imran Hazim Bin Abdullah Salim International Islamic University Malaysia, Malaysia
Azwa Nurnisya Binti Ayub International Islamic University Malaysia, Malaysia
Nuralya Medina Binti Mohammad Nizam International Islamic University Malaysia, Malaysia

DOI:

https://doi.org/10.57152/predatecs.v4i1.1856

Keywords:

Heart Disease, Machine Learning, Model Evaluation, Random Forest, Supervised Learning

Abstract

Heart disease is one of the leading causes of death worldwide, so early diagnosis is essential for effective treatment and prevention. This study evaluates the performance of five machine learning algorithms, namely Decision Tree, Naive Bayes, K-Nearest Neighbors (K-NN), Random Forest, and Support Vector Machine (SVM), using a heart disease dataset obtained from Kaggle. The dataset comprises 14 variables, including 13 attributes and 1 target variable. The models were tested on data split ratios of 70:30, 80:20, and 90:10, with performance measured by accuracy, precision, recall, and F1-score. The results showed that Random Forest performed best, achieving the highest accuracy of 98.54% at the 80:20 ratio. Decision Trees followed, yielding similar results, while K-NN performed the best at the 90:10 ratio, with a precision of 100%. In contrast, Naive Bayes performed lower due to high false positives. These results are in line with previous studies, confirming the effectiveness of Random Forest and Decision Trees in predicting heart disease risk. This research contributes to the development of reliable machine-learning models for early diagnosis and risk assessment of heart disease.

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