The most effective molecular features set selected above underwent similar data processing and mining steps but experienced a slight difference in the feature extraction stage. Instead of just using Random Forest to select essential features, eight different methods, consisting of Chi-squared (Chi2), Mutual information (Mutual), Random Forest (RF), Extra Tree (ExT), Adaboost (ADA), Gradient Boosting (Grad), XGBoost (XGB), and Logistic Regression (Logic), were performed to compare the performance of these models with baseline model, which was not performed feature extraction. The optimal algorithm was then used to reduce the dimension of the data.