Table 4.

Comparative analysis between the proposed hybrid GA-ML and existing work for COVID-19 detection based cough

Work	Dataset	Classifier	Results
Imran et al. (2020)	ESC-50	Multi-class classifier (CNN, SVM, and binary classifier)	Accuracy is 92.64
Verde et al. (2021)	Dataset collected by Cambridge University	ResNet	AUC is 84.6
Pahar et al. (2021)	Coswara	CNN, LSTM, ResNet50, and LSTM + SFS	Accuracy are 73.02, 73.78, 74.58, 92.91
Grant et al. (2021)	Crowdsourced	Random forest + DNN	AUC of 79.38 for detecting COVID-19 via speech sound analysis, and 75.75 for detecting COVID-19 via breathing sound analysis
Proposed CR19 framework	Coswara	Hybrid GA-ML (GA-LR, GA-LDR,GA-KNN,GA-CART,GA-NB, GA-SVM)	Accuracy are 90.78, 92.90, 95.74, 87.94, 81.56, and 92.198