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A HYBRID MACHINE LEARNING MODEL OPTIMIZED BY CHAOTIC DUNG BEETLE ALGORITHM FOR EXPLAINABLE OSTEOPOROSIS RISK PREDICTION.

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Title: A HYBRID MACHINE LEARNING MODEL OPTIMIZED BY CHAOTIC DUNG BEETLE ALGORITHM FOR EXPLAINABLE OSTEOPOROSIS RISK PREDICTION.
Authors: MENG, HAOYU1 (AUTHOR) mhy518426@163.com, HUANG, JIANJUN2 (AUTHOR) 614069155@qq.com
Source: Journal of Mechanics in Medicine & Biology. Apr2026, Vol. 26 Issue 3, p1-17. 17p.
Subjects: Machine learning, Optimization algorithms, Feature selection, Boosting algorithms, Risk assessment, Ensemble learning
Abstract: This study develops an explainable hybrid machine learning framework, which is optimized by an improved Chaotic Dung Beetle Optimization (CSDBO) algorithm, to enhance the accuracy of osteoporosis (OP) risk prediction. Based on 1537 clinical samples and 39 clinical and biochemical variables obtained from the Harvard Dataverse, the Boruta algorithm was employed to identify 12 key predictors. CSDBO was then used to perform intelligent hyperparameter optimization and model selection for multiple ensemble learning algorithms, including LightGBM, GBDT, and XGBoost. After optimization, LightGBM achieved the best performance, with a training AUC of approximately 0.998 and a test AUC exceeding 0.85. SHAP-based interpretability analysis indicated that femoral neck BMD, total lumbar T-score, and calcitriol were among the most influential factors. The proposed framework improves predictive accuracy and model stability while maintaining high interpretability, demonstrating potential value for clinical risk assessment and individualized intervention. Detailed algorithmic formulations and implementation procedures are provided in the Methods section. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A HYBRID MACHINE LEARNING MODEL OPTIMIZED BY CHAOTIC DUNG BEETLE ALGORITHM FOR EXPLAINABLE OSTEOPOROSIS RISK PREDICTION.
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  Data: <searchLink fieldCode="AR" term="%22MENG%2C+HAOYU%22">MENG, HAOYU</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mhy518426@163.com</i><br /><searchLink fieldCode="AR" term="%22HUANG%2C+JIANJUN%22">HUANG, JIANJUN</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 614069155@qq.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanics+in+Medicine+%26+Biology%22">Journal of Mechanics in Medicine & Biology</searchLink>. Apr2026, Vol. 26 Issue 3, p1-17. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink>
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  Label: Abstract
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  Data: This study develops an explainable hybrid machine learning framework, which is optimized by an improved Chaotic Dung Beetle Optimization (CSDBO) algorithm, to enhance the accuracy of osteoporosis (OP) risk prediction. Based on 1537 clinical samples and 39 clinical and biochemical variables obtained from the Harvard Dataverse, the Boruta algorithm was employed to identify 12 key predictors. CSDBO was then used to perform intelligent hyperparameter optimization and model selection for multiple ensemble learning algorithms, including LightGBM, GBDT, and XGBoost. After optimization, LightGBM achieved the best performance, with a training AUC of approximately 0.998 and a test AUC exceeding 0.85. SHAP-based interpretability analysis indicated that femoral neck BMD, total lumbar T-score, and calcitriol were among the most influential factors. The proposed framework improves predictive accuracy and model stability while maintaining high interpretability, demonstrating potential value for clinical risk assessment and individualized intervention. Detailed algorithmic formulations and implementation procedures are provided in the Methods section. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Mechanics in Medicine & Biology is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1142/S0219519426400178
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      – Code: eng
        Text: English
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        PageCount: 17
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    Subjects:
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Risk assessment
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      – SubjectFull: Ensemble learning
        Type: general
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      – TitleFull: A HYBRID MACHINE LEARNING MODEL OPTIMIZED BY CHAOTIC DUNG BEETLE ALGORITHM FOR EXPLAINABLE OSTEOPOROSIS RISK PREDICTION.
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            NameFull: MENG, HAOYU
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            NameFull: HUANG, JIANJUN
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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            – TitleFull: Journal of Mechanics in Medicine & Biology
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