Utilizing Wilcoxon statistical tests within the frameworks of meta-analysis and subgroup analysis, we selected the preferred feature type if it was statistically significantly better than the others in terms of coefficient of determination (R 2 ). Subsequently, feature selection or feature importance based dimensionality reduction was performed on seven algorithms including: analysis of variance (ANOVA), Mutual information (MUTUAL_INFO), Random Forest (RF), Extremely Randomized Trees (EXT), Adaptive boosting (ADA), Gradient boosting (GRAD) and XGBoost (XGB) to reasonably reduce the number of features. The refined training dataset was underwent evaluation through internal 10-fold cross-validation, repeated three times with the stratified scaffold splitting strategy, to select the most effective machine learning algorithm. Both feature and model selection processes were done using Wilcoxon statistical tests based on R 2 , root mean square error (RMSE) or mean absolute error (MAE). Finally, external validation was performed on the external validation dataset using the chosen settings and three metrics for generalization assertment.