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

Table 4 Predictive performance comparison of six machine learning models

Dataset	Model	AUC (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)	F1 score (95%CI)1
Train	RF	0.844 (0.810-0.878)	0.747 (0.689-0.803)	0.761 (0.708-0.811)	0.737 (0.690-0.782)
	KNN	0.785 (0.744-0.824)	0.719 (0.658-0.778)	0.714 (0.658-0.769)	0.700 (0.650-0.748)
	XGBoost	0.757 (0.714-0.799)	0.693 (0.632-0.752)	0.672 (0.614-0.730)	0.667 (0.615-0.714)
	LR	0.754 (0.710-0.795)	0.669 (0.608-0.730)	0.718 (0.663-0.772)	0.669 (0.616-0.718)
	MLP	0.754 (0.711-0.797)	0.683 (0.623-0.745)	0.722 (0.667-0.776)	0.680 (0.629-0.729)
	SVM	0.753 (0.708-0.795)	0.832 (0.780-0.880)	0.476 (0.413-0.537)	0.680 (0.633-0.725)
Validation	RF	0.766 (0.700-0.828)	0.628 (0.529-0.722)	0.703 (0.618-0.788)	0.633 (0.551-0.710)
	KNN	0.734 (0.665-0.800)	0.659 (0.560-0.755)	0.668 (0.582-0.755)	0.641 (0.557-0.718)
	XGBoost	0.749 (0.682-0.814)	0.638 (0.538-0.732)	0.731 (0.648-0.813)	0.651 (0.570-0.727)
	LR	0.755 (0.689-0.817)	0.617 (0.519-0.713)	0.704 (0.614-0.789)	0.626 (0.540-0.704)
	MLP	0.756 (0.685-0.816)	0.649 (0.553-0.743)	0.695 (0.607-0.780)	0.644 (0.562-0.717)
	SVM	0.753 (0.684-0.815)	0.831 (0.750-0.905)	0.505 (0.410-0.595)	0.687 (0.615-0.753)

¹The F1 score represents the harmonic mean of precision and recall, calculated as 2 × (precision × recall)/(precision + recall). This metric evaluates binary classification performance and fits data with mild imbalance between two groups.

AUC: Area under the receiver operating characteristic curve; KNN: K-nearest neighbors; LR: Logistic regression; MLP: Multilayer perceptron; RF: Random forest; SVM: Support vector machine; F1 score: F-measure (harmonic mean of precision and recall); XGBoost: Extreme gradient boosting.