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All Journal Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) International Journal of Global Operations Research Joutica : Journal of Informatic Unisla Jurnal Pengabdian Masyarakat Tapis Berseri Sainteks: Jurnal Sain dan Teknik
Reni Nursyanti
Universitas Bandar Lampung
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics

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A Metaheuristic wrapper approach to feature selection with genetic algorithm for enhancing XGBoost classification in diabetes prediction Alamsyah, Nur; Budiman; Danestiara, Venia Restreva; Yoga, Titan Parama; Nursyanti, Reni; Kaunang, Valencia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2366

Abstract

This study addressed the problem of selecting the most relevant features for improving the accuracy of diabetes classification using health indicator data. The research focused on a binary classification task based on the Behavioral Risk Factor Surveillance System dataset, which comprised over seventy thousand records and twenty-one predictive features related to individual health behaviors and conditions. A metaheuristic wrapper approach was developed by integrating a Genetic Algorithm for feature selection with an XGBoost classifier to evaluate the predictive quality of each feature subset. The fitness function was defined as the average classification accuracy obtained through cross-validation. In addition to feature selection, hyperparameter optimization of the XGBoost model was carried out using a Bayesian-based search strategy to further enhance performance. The proposed method successfully identified a subset of fourteen optimal features that contributed most significantly to the prediction of diabetes. The final model, combining the selected features and optimized parameters, achieved an accuracy of 0.753, outperforming baseline models trained with all features and models using features selected by deterministic methods. These results confirmed the effectiveness of combining evolutionary feature selection with model tuning to build efficient and interpretable predictive models for medical data classification. This approach demonstrated a practical solution for managing high-dimensional data in the context of chronic disease prediction.
Co-Authors Acep Hendra Akbar, Imannudin Anto Widianto Budiman Budiman Danestiara, Venia R Dani Setiawan Graha Prakarsa Ikhsan Nur, Muhammad Kaunang, Valencia Kaunang, Valencia Claudia Jennifer M, Marwodo Marwondo Nur Alamsyah Putra, Prayuda Mulyadi Rahadiyanti, Nira Ramadhani, Muhammad Wahyu Saputra, Renda Sandi Setiana, Elia Teofilus Hendrawan, Yesaya Titan Parama Yoga Venia Restreva Danestiara