Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0310218 |
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| _version_ | 1832540086788423680 |
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| author | Muhammad Rizwan Khurshid Sadaf Manzoor Touseef Sadiq Lal Hussain Mohammed Shahbaz Khan Ashit Kumar Dutta |
| author_facet | Muhammad Rizwan Khurshid Sadaf Manzoor Touseef Sadiq Lal Hussain Mohammed Shahbaz Khan Ashit Kumar Dutta |
| author_sort | Muhammad Rizwan Khurshid |
| collection | DOAJ |
| description | Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches. Leveraging Bayesian optimization to fine-tune XGBoost, researchers can harness the power of complex data analysis to improve predictive accuracy. By identifying key factors influencing diabetes risk, personalized prevention strategies can be developed, ultimately enhancing patient outcomes. Successful implementation requires meticulous data management, stringent ethical considerations, and seamless integration into healthcare systems. This study focused on optimizing the hyperparameters of an XGBoost ensemble machine learning model using Bayesian optimization. Compared to grid search XGBoost (accuracy: 97.24%, F1-score: 95.72%, MCC: 81.02%), the XGBoost with Bayesian optimization achieved slightly improved performance (accuracy: 97.26%, F1-score: 95.72%, MCC:81.18%). Although the improvements observed in this study are modest, the optimized XGBoost model with Bayesian optimization represents a promising step towards revolutionizing diabetes prevention and treatment. This approach holds significant potential to improve outcomes for individuals at risk of developing diabetes. |
| format | Article |
| id | doaj-art-076d88f17b5949798fbc840d8e658310 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-076d88f17b5949798fbc840d8e6583102025-02-05T05:32:16ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031021810.1371/journal.pone.0310218Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.Muhammad Rizwan KhurshidSadaf ManzoorTouseef SadiqLal HussainMohammed Shahbaz KhanAshit Kumar DuttaDiabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches. Leveraging Bayesian optimization to fine-tune XGBoost, researchers can harness the power of complex data analysis to improve predictive accuracy. By identifying key factors influencing diabetes risk, personalized prevention strategies can be developed, ultimately enhancing patient outcomes. Successful implementation requires meticulous data management, stringent ethical considerations, and seamless integration into healthcare systems. This study focused on optimizing the hyperparameters of an XGBoost ensemble machine learning model using Bayesian optimization. Compared to grid search XGBoost (accuracy: 97.24%, F1-score: 95.72%, MCC: 81.02%), the XGBoost with Bayesian optimization achieved slightly improved performance (accuracy: 97.26%, F1-score: 95.72%, MCC:81.18%). Although the improvements observed in this study are modest, the optimized XGBoost model with Bayesian optimization represents a promising step towards revolutionizing diabetes prevention and treatment. This approach holds significant potential to improve outcomes for individuals at risk of developing diabetes.https://doi.org/10.1371/journal.pone.0310218 |
| spellingShingle | Muhammad Rizwan Khurshid Sadaf Manzoor Touseef Sadiq Lal Hussain Mohammed Shahbaz Khan Ashit Kumar Dutta Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. PLoS ONE |
| title | Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. |
| title_full | Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. |
| title_fullStr | Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. |
| title_full_unstemmed | Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. |
| title_short | Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. |
| title_sort | unveiling diabetes onset optimized xgboost with bayesian optimization for enhanced prediction |
| url | https://doi.org/10.1371/journal.pone.0310218 |
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