Explainable machine learning model for predicting minor amputation in diabetic foot ulcers

Table 3 Hyperparameter optimization for predictive models

Model	Hyperparameter	Hyperparameter search space	Optimal hyperparameters
RF	Trees	200-500	300
	Mtry	2-10	2
	Min_n	20-50	30
KNN	Neighbors	3-11	11
XGBoost	Mtry	2-8	2
	Min_n	5-20	20
	Tree_depth	1-3	3
	Learn_rate	0.001-0.1	0.001
	Loss_reduction	0.001-1	0.001
	Sample_size	0.8-1	0.8
LR	Penalty	0.001-1	0.001
MLP	Hidden_units	15-24	15
	Penalty	0.001-1	1
	Epochs	50-150	50
SVM	Cost	0.00001-100000	0.031
	Rbf_sigma	0.0001-0.1	0.003

Cost: Regularization parameter controlling the margin error trade-off; Epochs: Number of complete passes through the training dataset; Hidden_units: Number of neurons in the hidden layer; KNN: K-nearest neighbors; Learn_rate: Learning rate shrinking weights to prevent overfitting; Loss_reduction: Minimum loss reduction required to split a leaf node; LR: Logistic regression; Min_n: Minimum number of data points required in a node to split further; MLP: Multilayer perceptron; Mtry: Number of predictors randomly sampled at each split; Neighbors: Number of nearest neighbors; Penalty: Regularization penalty term; Rbf_sigma: Kernel coefficient for the radial basis function; RF: Random forest; Sample_size: Subsample ratio of the training instances; SVM: Support vector machine; Trees: Total number of decision trees in an ensemble; XGBoost: Extreme gradient boosting.