Abstract
The aim of this study was to compare the performance of 4 machine learning models-Lasso regression model, random forest model, Boruta algorithm model, and the Boruta algorithm combined with Lasso regression-in predicting stroke risk among hypertensive patients. The study evaluated the strengths and weaknesses of each model to provide a more clinically valuable prediction model for stroke risk. The study included 3472 hypertensive patients, of which 312 had experienced a stroke, and 3160 had not. Various health indicators were analyzed using Lasso regression, random forest, Boruta algorithm, and the Boruta algorithm combined with Lasso regression. Model performance was evaluated based on the area under the curve (AUC) of the receiver operating characteristic curve, the precision-recall curve, calibration curve, and decision curve analysis to assess classification ability, precision, calibration, and clinical benefit. The Lasso regression and Boruta algorithm models both have an AUC of 0.716, making them the best-performing models in terms of classification ability. The Boruta algorithm combined with Lasso regression model has an AUC of 0.705, slightly lower than the previous 2 models but still shows good predictive capability, with better interpretability due to feature selection. The random forest model has an AUC of 0.626, which is the lowest among the models, indicating weaker classification performance compared to the others. Among the 4 models, the Lasso regression model and Boruta algorithm model performed similarly in terms of classification ability, both demonstrating moderate predictive power, while the random forest model performed relatively poorly. The Boruta combined with Lasso regression model was precise in variable selection but had limited clinical utility. Therefore, the Lasso regression model appears to be the most balanced in predicting stroke risk and is the recommended model based on this study.