Selected article for: "accuracy specificity sensitivity and confusion matrix"
Author: Rahul Kumar; Ridhi Arora; Vipul Bansal; Vinodh J Sahayasheela; Himanshu Buckchash; Javed Imran; Narayanan Narayanan; Ganesh N Pandian; Balasubramanian Raman
Title: Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers
  • Document date: 2020_4_17
    • ID: 59ghorzf_27
    Snippet: To evaluate the efficacy of the model, the confusion matrix along with Area under Curve (AUC) 32 are estimated and gives an understanding of the proposed methodology and its potential for detailed classification. The classification model's usefulness and productivity were measured using the traditional metrics of accuracy, precision, and recall. Precision is the calculation of the model's correct predictions all over all predictions. The classifi.....
    Document: To evaluate the efficacy of the model, the confusion matrix along with Area under Curve (AUC) 32 are estimated and gives an understanding of the proposed methodology and its potential for detailed classification. The classification model's usefulness and productivity were measured using the traditional metrics of accuracy, precision, and recall. Precision is the calculation of the model's correct predictions all over all predictions. The classification of COVID-19 patients and Normal or Pneumonia patients and between Normal patients and Pneumonia is termed as Accuracy, Sensitivity, Specificity, and F1-score are represented mathematically in terms of confusion matrix as given in Equations 1, 2, 3, and 4, respectively.

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