Table 4.

Glomerular classification performance for top feature sets and classifiers on the held-out test set

Classification Metrics	Feature Selection Method	Glomerular Classification
		Linear Discriminant Analysis	Random Forest	Logistic Regression
Accuracy	All features	60	58	71
	ANOVA	65	71	65
	GFI	77a	69	75
	mRMR	60	62	65
Precision	All features	58	53	70
	ANOVA	64	71	65
	GFI	77	68	77a
	mRMR	59	59	64
Recall	All features	60	56	70
	ANOVA	63	70	64
	GFI	76a	67	74
	mRMR	59	60	64
F1-score	All features	59	55	70
	ANOVA	63	70	64
	GFI	76a	68	76
	mRMR	59	60	64

The four feature selection methods analyzed were: all features, ANOVA F-value, GFI, and mRMR. The three classifiers analyzed were LDA, RF, and LR. Accuracy, precision, recall, and F1-score classification performance metrics were evaluated. ANOVA, ANOVA F-value; GFI, Gini feature importance; LDA, linear discriminant analysis; RF, random forest; LR, logistic regression; mRMR, maximum relevance minimum redundancy.

^aIndicates the model that achieved the best classification performance.