Stroke Management and Analysis Risk Tool (SMART): An interpretable clinical application for diabetes-related stroke prediction

Background and aims: The growing global burden of diabetes and stroke poses a significant public health challenge. This study aims to analyze factors and create an interpretable stroke prediction model for diabetic patients. Methods and results: Data from 20,014 patients were collected from the Affiliated Drum Tower Hospital, Medical School of Nanjing University, between 2021 and 2022. After handling the missing values, feature engineering included LASSO, SVM-RFE, and multi-factor regression techniques. The dataset was split 8:2 for training and testing, with the Synthetic Minority Oversampling Technique (SMOTE) to balance classes. Various machine learning and deep learning techniques, such as Random Forest (RF) and deep neural networks (DNN), have been utilized for model training. SHAP and a dedicated website showed the interpretability and practicality of the model. This study identified 11 factors influencing stroke incidence, with the RF and DNN algorithms achieving AUC values of 0.95 and 0.91, respectively. The Stroke Management and Analysis Risk Tool (SMART) was developed for clinical use. Primary endpoint: The predictive performance of SMART in assessing stroke risk in diabetic patients was evaluated using AUC. Secondary endpoints: Evaluated accuracy (precision, recall, F1-score), interpretability via SHAP values, and clinical utility, emphasizing user interface. Statistical analysis of EHR data using univariate and multivariate methods, with model validation on a separate test set. Conclusions: An interpretable stroke-predictive model was created for patients with diabetes. This model proposes that standard clinical and laboratory parameters can predict the stroke risk in individuals with diabetes.