Figure 2.

The experimental procedures. The analysis began with a 2-stage feature selection process. In the first stage, the conventional logistic regression analysis was employed to eliminate those features that were uncorrelated to the outcome variable. In the second stage, the proposed DT-based method along with two advanced multivariate analysis methods, namely being the least absolute shrinkage and selection operator (LASSO) method⁴⁰ and the ensemble variant of minimum redundancy maximum relevance (mRMRe) method^41,42, were employed to generate three 6-variable feature sets. Then, these three 6-variable feature sets were employed to build the DT, LR, and DNN prediction models. Finally, the performance of the alternative prediction models was evaluated based on the 10-fold cross validation process. *CVA cerebrovascular accident.