ML algorithms used in this study
REGRESSION
MLP is an artificial neural network that maps input data to appropriate outputs using an input layer, hidden layer, and output layer, each connected by a synaptic weight matrix and with nonlinear activation functions and trained via backpropagation [Random forest is an efficient ensemble technique that contains multiple decision trees generated from combined optimization decision trees, useful for classification and regression, and preventing overfitting with high accuracy even for incomplete datasets [Logistic regression is a statistical approach and supervised ML algorithm used for classification problems by mapping features to categorical targets and predicting the probability of a new case belonging to a target class [LightGBM is a high-performing gradient boosting framework that utilizes tree-based learning algorithms and includes Gradient-based One-Side Sampling and Exclusive Feature Bundling methods for selective sampling and reduced dimensionality [SVM is a versatile algorithm that can address regression, binary, and multi-class classification problems by identifying a hyperplane that maximizes the distance between classes in the feature space [KNN is a non-parametric, supervised learning classifier that predicts the grouping of an individual data point using proximity to other data points [