Figure 1.

The flow chart of model development and validation process. Subject data were randomized into a training set and a test set. The training set was 70% of the data and the test set was 30% of the data. For the training data set, the tenfold cross validation procedure was used to train and build 5 machine learning models (i.e., the RF, KNN, ANN, SVM and LG) in which the data was randomly split into 10 groups (9 groups for training and 1 group for validation). The tenfold cross validation process repeated until all 10 groups of data were trained and validated. The tenfold cross validation process was performed for all 5 machine learning models. After the 5 models were built, the test data set was entered into the 5 models to determine the model performance.