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Enhancing Early Heart Disease Detection Through Comparative Analysis of Random Forest, Decision Tree, and K-NN Models

ITJRD : IT Journal Research and Development [ISSN 2528 - 4061]
May 23, 2026 by
Enhancing Early Heart Disease Detection Through Comparative Analysis of Random Forest, Decision Tree, and K-NN Models
kelvin leonardi

Abstract


Heart disease is a leading cause of mortality worldwide and its rising prevalence challenges health systems. This study evaluates Decision Tree, k Nearest Neighbors, and Random Forest using the Heart Failure Prediction Dataset from Kaggle with 918 records and 12 demographic, clinical, and lifestyle features. The target variable indicates the presence of heart disease. Data preprocessing included cleaning, transformation, and scaling. Hyperparameters were tuned with stratified five fold cross validation to prevent data leakage. Performance was assessed using accuracy, precision, recall, F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient, and Brier score each estimated with 95 percent confidence intervals via bootstrap. k Nearest Neighbors achieved the highest accuracy at 90.2 percent, followed by Random Forest at 87.5 percent and Decision Tree at 85.3 percent. Calibration and decision curve analyses indicated that k Nearest Neighbors and Random Forest provided better calibrated probabilities and higher clinical utility across plausible thresholds. The study offers a reproducible evaluation pipeline and supports the use of machine learning for early detection of heart disease while encouraging future work on larger datasets and more advanced models.

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Kelvin Leonardi Kohsasih,
Kelvin Leonardi Kohsasih

STMIK TIME

Daniel Smith Sunario

STMIK TIME

Alvin Alvin

STMIK TIME

Fedro Laurendio

STMIK TIME

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Citation:

Kohsasih, K. L., Smith Sunario, D., Alvin, A., & Laurendio, F. (2025). Enhancing Early Heart Disease Detection Through Comparative Analysis of Random Forest, Decision Tree, and K-NN Models . IT Journal Research and Development, 10(2), 66–77. https://doi.org/10.25299/itjrd.2025.24703

Publication:
Vol. 10 No. 2 (2025): ITJRD: Journal Research and Development
DOI:
https://doi.org/10.25299/itjrd.2025.24703

Copyright:

Copyright (c) 2025 Kelvin Leonardi Kohsasih, Daniel Smith Sunario, Alvin Alvin, Fedro Laurendio
Enhancing Early Heart Disease Detection Through Comparative Analysis of Random Forest, Decision Tree, and K-NN Models
kelvin leonardi May 23, 2026
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