Comparing Different Supervised Machine Learning Accuracy on Analyzing COVID-19 Data using ANOVA Test

COVID-19 belongs to the genus of Coronaviridae, has emerged and affected the whole world. A virus without a vaccine creates unexpected havoc in human life, financial and economic systems of every country around the world. This disease carries a heavy workload for doctors and health workers. This workload can be reduced by machine learning and the development of computer-aided diagnostic systems. Any scientific study of this disease will help manage this disease as soon as possible. In this experiment, support vector machine, decision tree, and neural network classifiers have been built for COVID-19 dataset and the accuracies among those classifiers have been analyzed by ANOVA test. First, we performed data preprocessing to handle the missing values and categorical data. Second, we performed feature selection using chi-squared statistics and mutual-information statistics methods. Third, we split the data into 4 train-sets and test-sets for performing 4-fold cross validation. And lastly, we built SVM, DT, and NN models and started the experiment. In the experiment without feature selection, accuracy results among SVM, DT, and NN is stastistically significant (reject the null hypothesis). Otherwise, in the experiment with feature selection, accuracy results among SVM, DT, and NN is not stastistically significant (accept the null hypothesis). Both experiments are tested by ANOVA test. In this experiments, neural network classifiers (both without and with feature selection) have better accuracy result (98,16% and 97,08%) than SVM (96,63% and 96,72%) and DT (98,08% and 96,89%) for the dataset used in this experiment.