Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus
ObjectiveOsteoporosis is a common complication in patients with type 2 diabetes mellitus (T2DM), yet its screening rate remains low. This study aimed to develop and validate a cost-effective and interpretable machine learning (ML) model to predict the risk of osteoporosis in patients with T2DM.Metho...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1611499/full |
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| author | Qipeng Wei Zihao Liu Xiaofeng Chen Hao Li Weijun Guo Qingyan Huang Jinxiang Zhan Shiji Chen Dongling Cai Dongling Cai |
| author_facet | Qipeng Wei Zihao Liu Xiaofeng Chen Hao Li Weijun Guo Qingyan Huang Jinxiang Zhan Shiji Chen Dongling Cai Dongling Cai |
| author_sort | Qipeng Wei |
| collection | DOAJ |
| description | ObjectiveOsteoporosis is a common complication in patients with type 2 diabetes mellitus (T2DM), yet its screening rate remains low. This study aimed to develop and validate a cost-effective and interpretable machine learning (ML) model to predict the risk of osteoporosis in patients with T2DM.MethodsThis retrospective study included 1560 inpatients who underwent dual-energy X-ray absorptiometry (DXA) between January 2022 and December 2023 at Panyu Hospital of Chinese Medicine. Demographic information and laboratory test results obtained within 24 hours of hospital admission were collected. Potential predictive features were identified using univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Eight supervised ML algorithms were applied to construct predictive models. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), accuracy, sensitivity, specificity, and F1 score. The SHapley Additive exPlanations (SHAP) method was used to interpret the model and visualize feature importance.ResultsTen predictive features were selected based on the intersection of the three feature selection methods. Among the tested models, logistic regression achieved the best overall performance, with an AUC of 0.812, an accuracy of 0.762, a sensitivity of 0.809, a specificity of 0.761, and an F1 score of 0.771 in the validation set. Calibration plots and DCA curves demonstrated good agreement and the highest net clinical benefit. SHAP analysis identified age, sex, alkaline phosphatase, uric acid, hemoglobin, and neutrophil count as the six most influential features. An easy-to-use, web-based risk calculator was developed based on the logistic model and is available at: https://t2dm.shinyapps.io/t2dm-osteoporosis/.ConclusionWe developed an interpretable and accessible ML-based online tool that enables preliminary screening of osteoporosis risk in patients with T2DM using routine blood indicators. This tool may assist clinicians in early risk identification and reduce the underdiagnosis of osteoporosis. |
| format | Article |
| id | doaj-art-a3331e1640f542bebfbb479cbdef4366 |
| institution | DOAJ |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-a3331e1640f542bebfbb479cbdef43662025-08-20T02:57:30ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-08-011610.3389/fendo.2025.16114991611499Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitusQipeng Wei0Zihao Liu1Xiaofeng Chen2Hao Li3Weijun Guo4Qingyan Huang5Jinxiang Zhan6Shiji Chen7Dongling Cai8Dongling Cai9Department of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaPanyu Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaPanyu Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, ChinaDepartment of Orthopedics, Panyu Hospital of Chinese Medicine, Guangzhou, ChinaPanyu Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, ChinaObjectiveOsteoporosis is a common complication in patients with type 2 diabetes mellitus (T2DM), yet its screening rate remains low. This study aimed to develop and validate a cost-effective and interpretable machine learning (ML) model to predict the risk of osteoporosis in patients with T2DM.MethodsThis retrospective study included 1560 inpatients who underwent dual-energy X-ray absorptiometry (DXA) between January 2022 and December 2023 at Panyu Hospital of Chinese Medicine. Demographic information and laboratory test results obtained within 24 hours of hospital admission were collected. Potential predictive features were identified using univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Eight supervised ML algorithms were applied to construct predictive models. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), accuracy, sensitivity, specificity, and F1 score. The SHapley Additive exPlanations (SHAP) method was used to interpret the model and visualize feature importance.ResultsTen predictive features were selected based on the intersection of the three feature selection methods. Among the tested models, logistic regression achieved the best overall performance, with an AUC of 0.812, an accuracy of 0.762, a sensitivity of 0.809, a specificity of 0.761, and an F1 score of 0.771 in the validation set. Calibration plots and DCA curves demonstrated good agreement and the highest net clinical benefit. SHAP analysis identified age, sex, alkaline phosphatase, uric acid, hemoglobin, and neutrophil count as the six most influential features. An easy-to-use, web-based risk calculator was developed based on the logistic model and is available at: https://t2dm.shinyapps.io/t2dm-osteoporosis/.ConclusionWe developed an interpretable and accessible ML-based online tool that enables preliminary screening of osteoporosis risk in patients with T2DM using routine blood indicators. This tool may assist clinicians in early risk identification and reduce the underdiagnosis of osteoporosis.https://www.frontiersin.org/articles/10.3389/fendo.2025.1611499/fullosteoporosistype 2 diabetes mellitusexplainable machine learningpredictive modelrisk assessment |
| spellingShingle | Qipeng Wei Zihao Liu Xiaofeng Chen Hao Li Weijun Guo Qingyan Huang Jinxiang Zhan Shiji Chen Dongling Cai Dongling Cai Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus Frontiers in Endocrinology osteoporosis type 2 diabetes mellitus explainable machine learning predictive model risk assessment |
| title | Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus |
| title_full | Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus |
| title_fullStr | Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus |
| title_full_unstemmed | Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus |
| title_short | Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus |
| title_sort | development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus |
| topic | osteoporosis type 2 diabetes mellitus explainable machine learning predictive model risk assessment |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1611499/full |
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