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All Journal Journal of Information Technology and Computer Science
Niken Fitria Apriani
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Heart Disease Prediction System Employing Machine Learning Rian Nopiardi; Raka Dimas Saputra; Niken Fitria Apriani; Al Hafiz Akbar Maulana Siagian; Shidiq Al Hakim; Fatyanosa, Tirana Noor
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102926

Abstract

Heart disease places a significant strain on healthcare systems. Moreover, it kills millions of people each year, which is a leading cause of death worldwide. Hypertension, diabetes, unhealthy lifestyles, and genetic predispositions are all risk factors for heart disease. However, it is not easy to identify a heart disease. For this reason, helping in identifying the heart disease is important to prevent a death, e.g., caused by a heart attack. In this study, we aim to develop a heart disease prediction system. The system is developed according to the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework by employing machine learning algorithms. In this work, Logistic Regression (LR) and Random Forest (RF) are utilized as our machine learning algorithms for classifying heart disease using a heart disease dataset from Kaggle. Our results show LR has an AUC value of 0.921 and F1-Score 0.89 that outperforms RF with an AUC value of 0.920 and F1-Score 0.84 in this work. Then, we select LR to be applied to the developed heart disease prediction system.
Co-Authors Al Hafiz Akbar Maulana Siagian Raka Dimas Saputra Rian Nopiardi Shidiq Al Hakim Tirana Noor Fatyanosa, Tirana Noor