Table 2.

Metric scores of eight individual classifiers as well as soft voting classifier consisting of random forest, logistic regression, extreme gradient boosting, and multilayer perceptron trained with sarcomere length transient features. The best-performing classifier is highlighted in red.

ML classifiers	Sensitivity	Specificity	Precision	Accuracy	F1-score
Random Forest	0.8679	0.8082	0.7667	0.8333	0.8142
Support vector machine	0.8824	0.80	0.75	0.8333	0.8108
K-nearest neighbors	0.8478	0.7375	0.65	0.7778	0.7358
Decision tree	0.8889	0.6869	0.5333	0.746	0.6667
Logistic regression	0.7966	0.806	0.7833	0.8016	0.7899
Adaptive boosting	0.7385	0.8033	0.80	0.7698	0.768
Extreme gradient boosting	0.8254	0.8730	0.8667	0.8492	0.8455
Multilayer perceptron	0.8644	0.8657	0.85	0.8651	0.8571
Soft voting	0.8909	0.8451	0.8167	0.8651	0.8522