Selected article for: "AUC ROC curve area and classifier performance"
Author: Sally M. ELGhamrawy; Abou Ellah Hassanien
Title: Diagnosis and Prediction Model for COVID19 Patients Response to Treatment based on Convolutional Neural Networks and Whale Optimization Algorithm Using CT Images
  • Document date: 2020_4_21
    • ID: jir00627_90
    Snippet: The copyright holder for this preprint . https://doi.org/10.1101/2020.04. 16.20063990 doi: medRxiv preprint random predictions with higher accuracy than the calculated predictions. The performance of three classifiers (SVM, NB and, DA) is tested on the dataset with relevant features extracted from FSWOA phase. The overall accuracy, F-score, G-mean, and the area under the ROC curve (AUC) are evaluated for each classifier, as shown in table 2. The .....
    Document: The copyright holder for this preprint . https://doi.org/10.1101/2020.04. 16.20063990 doi: medRxiv preprint random predictions with higher accuracy than the calculated predictions. The performance of three classifiers (SVM, NB and, DA) is tested on the dataset with relevant features extracted from FSWOA phase. The overall accuracy, F-score, G-mean, and the area under the ROC curve (AUC) are evaluated for each classifier, as shown in table 2. The results indicate that the performance of the SVM classifier is the highest with a value of 97.14 % followed by the NB with a value of 95.99%, then the DA one with a value of 94.1%.

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