A Risk Assessment Tool for Identifying Osteoporosis in Older Women
                    with Type 2 Diabetes Mellitus

Pan, Feng; Gao, Lei; Shi, Xiaoting; Zhang, Mingrui; Li, Man; Yang, Haiyan; Hu, Yun

doi:10.1055/a-1702-5168

Cited by 1 publication

(1 citation statement)

References 42 publications

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“…More recently, ML algorithms have been applied to predict osteoporosis risk in T2DM ( Table S1 ). Recent research by Pan et al, 14 utilized the least absolute shrinkage and selection operator regularization (LASSO) model to develop a risk assessment tool for detecting osteoporosis in older women with T2DM based on age, BMI, serum sex hormone-binding globulin (SHBG), and CTX. In another SVM (support vector machine) model developed by Wang et al, 15 based on sex, age, BMI (body mass index), TP1NP (total procollagen I N-terminal propeptide), and OSTEOC (osteocalcin), the accuracy of the final model in predicting osteoporosis was greater than 88%.…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Models for Predicting Osteoporosis in Patients with Type 2 Diabetes Mellitus—A Preliminary Study

Wu¹,

Zhai²,

Chang³

et al. 2023

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Purpose Diagnosing osteoporosis in T2DM based on bone mineral density (BMD) remains challenging. We sought to develop prediction models employing machine learning algorithms for use as screening instruments for osteoporosis in T2DM patients. Patients and Methods Data were collected from 433 participants and analyzed using nine categorical machine learning algorithms to select features based on demographic and clinical variables. Multiple classification models were compared using the area under the receiver operating characteristic curve (ROC-AUC), accuracy, sensitivity, specificity, the average precision (AP), precision, F1 score, precision-recall curves, calibration plots, and decision curve analysis (DCA) to determine the best model. In addition, 5-fold cross-validation was utilized to optimize the model, followed by an evaluation of feature significance using Shapley Additive exPlanations (SHAP). Using latent class analysis (LCA), distinct subpopulations were identified by constructing several discrete clusters. Results In this study, nine feature variables were identified to construct predictive models for osteoporosis in individuals with T2DM. The machine learning algorithms achieved an AP range of 0.444–1.000. The XGBoost model was selected as the final prediction model with an AUROC of 0.940 in the training set, 0.772 in the validation set for 5-fold cross-validation, and 0.872 in the test set. Using SHAP methodology, 25(OH)D was identified as the most important risk factor. Additionally, a 3-Class model was constructed using LCA, which categorized individuals into high, medium, and low-risk groups. Conclusion Our study developed a predictive model with high accuracy and clinical validity for predicting osteoporosis in type 2 diabetes patients. We also identified three subpopulations with varying osteoporosis risk using clustering. However, limited sample size warrants cautious interpretation of results, and validation in larger cohorts is needed.

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Section: Introductionmentioning

confidence: 99%