Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization
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...
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| Format: | Article |
| Language: | English |
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Ikatan Ahli Informatika Indonesia
2025-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6307 |
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| author | Syaiful Anam Imam Nurhadi Purwanto Dwi Mifta Mahanani Feby Indriana Yusuf Hady Rasikhun |
| author_facet | Syaiful Anam Imam Nurhadi Purwanto Dwi Mifta Mahanani Feby Indriana Yusuf Hady Rasikhun |
| author_sort | Syaiful Anam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-31cb3f95908a49f8a0794943d8dd94e1 |
| institution | DOAJ |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-31cb3f95908a49f8a0794943d8dd94e12025-08-20T03:15:47ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-06-019353554310.29207/resti.v9i3.63076307Health Risk Classification Using XGBoost with Bayesian Hyperparameter OptimizationSyaiful Anam0Imam Nurhadi Purwanto1Dwi Mifta Mahanani2Feby Indriana Yusuf3Hady Rasikhun4Brawijaya UniversityBrawijaya UniversityBrawijaya UniversityBrawijaya UniversityMuhammadiyah University of MataramHealth 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.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6307health risk classificationhyperparametersoptimizationxgboost |
| spellingShingle | Syaiful Anam Imam Nurhadi Purwanto Dwi Mifta Mahanani Feby Indriana Yusuf Hady Rasikhun Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) health risk classification hyperparameters optimization xgboost |
| title | Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization |
| title_full | Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization |
| title_fullStr | Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization |
| title_full_unstemmed | Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization |
| title_short | Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization |
| title_sort | health risk classification using xgboost with bayesian hyperparameter optimization |
| topic | health risk classification hyperparameters optimization xgboost |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6307 |
| work_keys_str_mv | AT syaifulanam healthriskclassificationusingxgboostwithbayesianhyperparameteroptimization AT imamnurhadipurwanto healthriskclassificationusingxgboostwithbayesianhyperparameteroptimization AT dwimiftamahanani healthriskclassificationusingxgboostwithbayesianhyperparameteroptimization AT febyindrianayusuf healthriskclassificationusingxgboostwithbayesianhyperparameteroptimization AT hadyrasikhun healthriskclassificationusingxgboostwithbayesianhyperparameteroptimization |