Table 3.

Metric scores of eight individual classifiers as well as soft voting classifier consisting of random forest, logistic regression, K-nearest neighbors, and multilayer perceptron trained with calcium transient features. The best-performing classifier is highlighted in red.

ML classifiers	Sensitivity	Specificity	Precision	Accuracy	F1-score
Random Forest	0.8519	0.8226	0.807	0.8362	0.8288
Support vector machine	0.8214	0.8167	0.807	0.819	0.8142
K-nearest neighbors	0.8182	0.8033	0.7895	0.8103	0.8036
Decision tree	0.7719	0.7797	0.7719	0.7759	0.7719
Logistic regression	0.8750	0.8667	0.8596	0.8707	0.8673
Adaptive boosting	0.7759	0.7931	0.7895	0.7845	0.7826
Extreme gradient boosting	0.7544	0.7627	0.7544	0.7586	0.7544
Multilayer perceptron	0.8596	0.8644	0.8596	0.8621	0.8596
Soft voting	0.8909	0.8689	0.8596	0.8793	0.8750