Towards Machine Learning Algorithm for Screening Prediction of COVID-19 Patients

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Siwakorn Banluesapy
Waraporn Jirapanthong


his research aims to develop a web-based application for screening COVID-19 patients by applying with machine learning techniques. We have applied the techniques to classify the data and then compare the algorithm’s efficiency in prediction of COVID-19 patient screening. The data on 1,608,923 patients from the Department of Disease Control are accumulated. In particular, the data from January 1, 2020 to October 1, 2021 are learnt by three classification algorithms: Random Forest, Neural network, and Naive Bayes. The performance of predictive techniques between features are compared. The models learnt and generated based on different three algorithms are tested by the Split Test method. The data are randomly divided into two parts: Training Data (80%) and Test Data (20%) by using Orange Canvas. The models are then experimented to determine the performance test results with the highest accuracy. As the results, it is found that the accuracy rates of Random Forest, Neural Network, and Naïve Bayes are 93.33%, 92.7%, and 92.1%, respectively. The model based on Random Forest technique with the highest value of accuracy is then applied in the application for screening COVID-19 patients.


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How to Cite
S. Banluesapy and W. Jirapanthong, “Towards Machine Learning Algorithm for Screening Prediction of COVID-19 Patients ”, JIST, vol. 12, no. 1, pp. 47–60, Jun. 2022.
Academic Article: Soft Computing (Detail in Scope of Journal)


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