Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images

Sally M. ELGhamrawy; Abou Ellah Hassanien

Considering the SVM, the F-score value is 96.84% meaning that both TP and TN are high, simultaneously. Also, the G-mean value (96.93 %) is high, which means that both TPR and TNR are high; thus, indicating that the overall classification performance of the imbalanced dataset is good. This fact is emphasized by the high value of the AUC (0.9957). A Sensitivity score of the COVID of 96.27% indicates the portion of the COVID that is predicted correctly. Likewise, a Specificity score of 97.6% indicates the portion of the Non COVID sample that is predicted correctly, (447 out of 458). This indicates that the SVM is more accurate in predicting the Non COVID in this case. When it comes to the performance of the NB classifier, it is seen that the F-score value is lower than that of the G-mean; however, both are relatively high (above 95.63 %) indicating the goodness of the performance. A high AUC value of 0.9857 confirms an effectiveness of performance. A sensitivity of 97.51% and 95.29 % for the COVID and Non COVID, respectively, indicate that the NB predicts the COVID class more effectively than Non COVID (for this dataset), the contrary of the SVM. As for the DA classifier, the F-score and the G-mean values are close to each other (around 94%), which gives indication that the DA performance is compatible.