Decision tree	Criterion: Gini; splitter: best; max depth: 8; minimum samples at a leaf node: 2; maximum number of features: none; class weights: balanced; random state: 1
Random forest	Number of trees: 500; criterion: Gini; maximum depth: 5; minimum samples at a leaf node: 2; minimum samples to split: 2; maximum number of features: auto; class weights: balanced; random state: 1
AdaBoost	Maximum number of estimators: 350; base estimator: decision tree (class weights: balanced; max depth: 1); learning rate: 1; random state: 1
Support vector machine	Regularization parameter: 2; kernel: polynomial; degree: 2; class weights: {0:1,1:26}; random state: 0
Logistic regression	Loss: binary cross-entropy; kernel initializer: Glorot uniform; kernel optimiser: Adam; kernel regularizer: l2 (λ = 1e−5); learning rate: 1e−4 for epochs 1–50, 1e−5 for epochs 51–100, 1e−6 for epochs > 100; class weights: {0: 1, 1: 26}; batch size: 64; number of epochs: 700; callbacks: early stopping (monitor: validation loss; patience: 4; restore best weights: true)
Neural network (one hidden layer)	Number of neurons: 5000; activation: relu; loss: binary cross-entropy; kernel initializer: Glorot uniform; kernel optimiser: Adam; kernel regularizer: l2 (λ = 1e−4); learning rate: 1e−5 for epochs 1–180, 1e−6 for epochs > 180; class weights: {0: 1, 1: 26}; batch size: 64; number of epochs: 300; callbacks: early stopping (monitor: validation loss; patience: 4; restore best weights: true)
Neural network (five hidden layers)	Number of neurons: 1000–500–200–100–50; activation: relu; loss: binary cross-entropy; kernel initializer: Glorot uniform; kernel optimiser: Adam; kernel regularizer: dropout (rate = 1.5e−1); learning rate: 1e−5 for epochs 1–100, 1e−6 for epochs 101–150, 1e−7 for epochs > 150; class weights: {0: 1, 1: 26}; batch size: 64; number of epochs: 200; callbacks: early stopping (monitor: validation loss; patience: 4; restore best weights: true)

Source: Author’s own work