02269nas a2200241   4500000000100000000000100001008004100002260001200043653001800055653001900073653002400092653002200116653002100138100001800159700001700177700001500194245008500209856005800294300000900352490001300361520163900374022001402013       9998                            d  c01/202510aBlood Samples10aClassification10aCOVID-19 Prediction10aFeature Selection10aMachine Learning1 aA. Suruliandi1 aR. Ame Rayan1 aS. P. Raja00aPrediction of COVID-19 Using a Clinical Dataset With Machine Learning Approaches  uhttps://www.ijimai.org/journal/bibcite/reference/3529  a1-170 vIn press3 aCOVID-19 is an infectious disease that spreads quickly from person to another. The pandemic, which spread worldwide over time, presents huge risks in terms of blood clotting, breathing problems and heart attacks, sometimes with fatal consequences if not detected early. The PCR test, CT scans, X-rays, and blood tests are methods commonly employed to detect the disease, though the PCR test is, without question, considered the gold standard. The American Center for Disease Control and Prevention (CDC) reports that the PCR has an 80% accuracy rate. An alternative to the PCR is clinical data, which is less expensive, easy to collect, and offers better accuracy. Machine learning, with its rich feature selection and classification methods, helps detect COVID-19 at the earliest stages, using clinical test results. This research proposes a clinical dataset and offers a comparative analysis of feature selection and classification algorithms for detecting COVID-19. Filter-based feature selection methods such as the ANOVA-F, chi-square, mutual information and Pearson correlation, along with wrapperbased methods such as Recursive Feature Elimination (RFE) and Sequential Forward Selection (SFS) were used to choose a subset of features from the feature set. The selected features were thereafter applied to the Support Vector Machine (SVM), Naïve Bayes, K-NN (K-Nearest Neighbor) and Logistic Regression(LR) classification algorithms to detect Coronavirus Disease. The experimental results of the comparative study show that the clinical dataset provides better accuracy at 94.8%, with mutual information and the SVM classifier.  a1989-1660