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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika JIPM (Jurnal Ilmiah Pendidikan Matematika) Math Didactic: Jurnal Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Unisda Journal of Mathematics and Computer Science (UJMC) Transformasi : Jurnal Pendidikan Matematika dan Matematika JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Feby Indriana Yusuf
Universitas PGRI Banyuwangi
Humanities Computer Science & IT Education Engineering Mathematics Public Health Other

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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization Anam, Syaiful; Purwanto, Imam Nurhadi; Mahanani, Dwi Mifta; Yusuf, Feby Indriana; Rasikhun, Hady
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6307

Abstract

Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques. The XGBoost algorithm offers many advantages over the traditional methods for risk classification. Hyperparameter Optimization (HO) of XGBoost is critical for maximizing the performance of the XGBoost algorithm. The manual selection of hyperparameters requires a large amount of time and computational resources. Automatic HO is needed to avoid this problem. Several studies have shown that Bayesian Optimization (BO) works better than Grid Search (GS) or Random Search (RS). Based on these problems, this study proposes health risk classification using XGBoost with Bayesian Hyperparameters Optimization. The goal of this study is to reduce the time required to select the best XGBoost hyperparameters and improve the accuracy and generalization of XGBoost performance in health risk classification. The variables used were patient demographics and medical information, including age, blood pressure, cholesterol, and lifestyle variables. The experimental results show that the proposed approach outperforms other well-known ML techniques and the XGBoost method without HO. The average accuracy, precision, recall and f1-score produced by the proposed method are 0.926, 0.920, 0.928, and 0.923, respectively. However, improvements are needed to obtain a faster and more accurate method in the future.
Co-Authors Adi, Puti Zakiyah Raisa Anggraini, Dessy Anggraini, Dessy Anjasari, Dwi Hilda Anjasari, Dwi Hilda Arvianti, Aurellia A’la, Kevina Alal Barep Yohanes Bukhori, Hilmi Aziz Dwi Hilda Anjasari Dwi Mifta Mahanani, Dwi Mifta Dzurotul Mutimmah Eko Listiwikono Endang Wahyu Handamari Fitri Nurmasari Hady Rasikhun Heri Susanto Imam Nurhadi Purwanto Julianti, Nurma Haya Nadya Adharani Novi Prayekti Nurma Haya Julianti Permata, Wahyu Finda Permata, Wahyu Finda Rachmaniah Mirza Hariastuti Rahmi, Miftachul Rahmi, Miftachul Saiful Bukhori Saragih, Trecy Elisabet Tioralina Sari, Meylita Situmorang, Boy Nathanael Sulistiono Susanti, Reny Eka Evi Syaiful Anam Wiwik Handayani