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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknoinfo Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika CSRID (Computer Science Research and Its Development Journal) INTI Nusa Mandiri Jurnal Infortech Jurnal Bumigora Information Technology (BITe) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta Transformasi
Ummu Radiyah, Ummu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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OPTIMIZING RANDOM FOREST FOR HEART DISEASE PREDICTION THROUGH GENETIC ALGORITHM FEATURE SELECTION AND SMOTEENN Amin, Ruhul; Radiyah, Ummu
Jurnal Techno Nusa Mandiri Vol. 23 No. 1 (2026): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/k9qqym58

Abstract

Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for early prediction methods capable of accurately identifying individuals at risk. This study aims to develop a heart disease prediction model based on Random Forest by integrating a Genetic Algorithm for feature selection and SMOTEENN to address class imbalance. The research stages include data preprocessing, class balancing using SMOTEENN, feature selection using a Genetic Algorithm, Random Forest model development, and hyperparameter optimization using GridSearchCV, with recall as the primary evaluation metric. The initial Random Forest model demonstrated good predictive performance, achieving a recall of 0.94 for the positive class on the test set, while yielding a lower recall of 0.74 for the negative class, indicating an imbalance in classification performance across classes. Following the application of SMOTEENN, Genetic Algorithm-based feature selection, and hyperparameter optimization, the optimized model achieved a mean recall of 0.91 based on cross-validation. These findings indicate that the proposed approach maintains high predictive sensitivity while incorporating mechanisms to address class imbalance and identify relevant features. Therefore, the integration of Random Forest, SMOTEENN, and Genetic Algorithm demonstrates potential as a predictive approach to support the early detection of heart disease. However, as this study employs a non-local dataset, further validation using datasets representative of the target population is required before the model can be generalized or applied to specific populations.
Co-Authors Ade Christian Adi Supriyatna ahmad yani Alizah, Muhammad Dwison Assani, Moh. Yushi Atmawijaya, Rizky Gata, Windu HARIYANTO HARIYANTO Herputra, Alviando Daffa Ika Kurniawati Imam Budiawan Lita Sari Marita Mulyani, Astriana Nainggolan, Esron Rikardo Nugroho, Arifin Pujiastuti, Lise Rudiyan, Ari Ruhul Amin Salamatun Umma Sidik Sidik Sumanto Sumanto Susafa’ati, Susafa’ati Syarif Hidayatulloh