Development and validation of a machine learning-based diagnostic prediction model for frailty in older adults with...

Frailty is a significant risk factor for death and disability in older adults with diabetes. Early identification of frailty in this population is crucial for implementing timely interventions.

We analyzed national longitudinal data from the China Health and Retirement Longitudinal Study. Frailty status was defined using a modified Frailty Phenotype, and candidate predictors were selected based on published systematic reviews and meta-analyses. Six machine learning models were developed, including logistic regression, support vector machine, random forest, adaptive boosting, gradient boosting decision tree, and gradient boosting. Hyperparameters were tuned using grid search, and model validation was conducted via leave-one-out cross-validation and temporal validation. Model performance was evaluated using the area under the receiver operating characteristic curve, specificity, recall, precision, negative predictive value, accuracy, F1 score, decision curve analysis, calibration curve, and Brier score. The model's decision-making mechanism was interpreted using SHapley Additive exPlanations.

A total of 2366 older adults with diabetes were included in the analysis, of whom 1613 were assigned to the training set and 753 to the test set. The random forest model performed best in estimating probability of frailty (training set: AUC = 0.988, 95% CI: 0.983-0.991; test set: AUC = 0.987, 95% CI: 0.982-0.993). Model interpretation identified depression as the most influential predictor. The model has been deployed on a web-based platform (https://ff.magvel.top/).

This study developed and validated a machine learning-based diagnostic prediction model to estimate the probability of frailty in older adults with diabetes. The random forest model demonstrated promising performance, with good accuracy and potential clinical utility.