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Adam Prayogo Kuncoro
STMIK AMIKOM Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Optimizing XGBoost for Heart Disease Risk Classification Using Optuna and Random Search on the Behavioral Risk Factor Surveillance System (BRFSS) 2023 Dataset Dzaky, Muhammad; Kuncoro, Adam Prayogo; Riyanto, Riyanto
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11897

Abstract

Heart disease is a critical public health issue in Indonesia, contributing to approximately 1,5 million deaths annually. Although machine learning methods, particularly Extreme Gradient Boosting (XGBoost), have demonstrated strong performance in medical classification tasks, their optimization on large-scale and highly imbalanced health datasets remains underexplored. This study optimizes XGBoost for heart disease risk classification using the Behavioral Risk Factor Surveillance System (BRFSS) 2023 dataset, consisting of 290.156 samples after preprocessing. Two hyperparameter optimization approaches, Optuna and Random Search, are evaluated across three class imbalance handling techniques, namely class weighting, SMOTE, and Random Undersampling (RUS). Model evaluation focuses on AUC and recall to prioritize sensitivity in identifying individuals at risk. The results show that the OptunaRUS and RandomWeight models achieve the most stable performance, with OptunaRUS attaining an AUC of 83,06% and a recall of 75,69% on the test dataset. Feature importance analysis indicates that age range and hypertension are the most influential predictors. These findings confirm that hyperparameter optimization on large-scale health data improves model discriminative capability and generalization, while selective sampling strategies such as RUS provide more stable performance than generative methods in high-dimensional datasets.
Co-Authors Adi Purnomo Aditia Wardana, Aditia Afiana, Fiby Nur Akbari, Rifqy Willy Anshari, Muhammad Rifqi Arsi, Primandani Aulia Hamdi Basten, Geza Andika Basten, Geza Andika Damayanti, Wenti Risma Dhanar Intan Surya Saputra Dwi Krisbiantoro, Dwi Dzaky, Muhammad Ekhsanto, Bagus kurniawan Fadilah, Alif Nur Hidayat, Rizky Bagas Nur Hikmah, Hani Fatul Iftinani, Alifah Dafa Ikhsan, Ali Nur Irma Darmayanti Isnaini, Khairunnisak Khofiyah, Salma Ngarifatul Kusuma, Bagus Adhi Kusuma, Bagus Adhi Munawaroh, Afifah Lutfia Muntahar, Adnan Aditya Mustofa, Dinar Nandia, Jumiati Riskiyani Dwi Nofita Sari, Guti Julias Nugroho, Firdan Rafi Oktaviana, Luzi Dwi Riyanto Riyanto Riyanto Rujianto Eko Saputro Safitri, Dela Salsabiela, Ayuni Setiawan, Gilang Aji Sholikhatin, Siti Alvi Subarkah, Pungkas Suliswaningsih, Suliswaningsih Tarwoto, Tarwoto Tarwoto, Tarwoto Tarwoto, Tarwoto Wijaya, Anugerah Bagus