Paper Title
COVID-19 Prediction using an Efficient Symptom Question Mining Method

Questionnaires onCOVID-19 pandemic are becoming increasingly important for measuring COVID-19 cases that was designed to measure the symptoms to which individuals appraise situations in their lives as COVID-19 diagnosis.To avoid as much as several waves of infection cases and hospitalization cases identified, efficient tools tofacilitate the diagnosis of COVID-19 are needed. In this study, we propose an efficient method for designing predictors of COVID-19 cases using a small set of symptoms question items obtained by using novel machine learning models from existing dataset which is composed90,839 adultsCovid-19 negativeand 8,393 adultsCovid-19 positive who responses to the nationwide data publicly reportedby the Israeli Ministry of Health. For the independent-adult prediction, the training dataset contains 51,831 individuals (of whom 4769Covid-19 positive)and47,401 tested individuals (of whom 3624Covid-19 positive) serves as the test dataset. A predictor, PreCOVID19, composed of 8 features selected usingorthogonal experimental design bases on an orthogonal array is created for predicting COVID-19 caseswith 95.81 % training accuracy and the test accuracy is 95.87%. The web server is available at Keywords - Covid-19 Questionnaire,Symptoms Questions, Orthogonal Experimental Design, Prediction, XGBoot.