Machine learning and statistical modelling for prediction of Novel COVID-19 patients case study: Jordan

被引：0

作者：

Fayyoumi E. ^{[1
]}

Idwan S. ^{[1
]}

Aboshindi H. ^{[2
]}

机构：

[1] Department of Computer Science and Applications, Faculty of Prince Al Hussein Bin Abdullah II for Information Technology, Hashemite University, P.O. Box 330127, Zarqa

[2] P.O. Box 851275, Sweifieh, Amman

来源：

Fayyoumi, Ebaa | 2020年 / Science and Information Organization卷 / 11期

关键词：

Logistic regression; Machine learning; Multi-layer perceptron; Novel COVID-19; Support vector machine;

D O I：

10.14569/IJACSA.2020.0110518

中图分类号：

学科分类号：

摘要：

As of December 2019, the world's view on life has been changed due to ongoing COVID-19 pandemic. This requires the use of all kinds of technology to help identify coronavirus patients and control the spread of this disease. In this paper, an online questionnaire was developed as a tool to collect data. This data was used as an input for various prediction models based on statistical model (Logistic Regression, LR) and machine learning model (Support Vector Machine, SVM, and Multi-Layer Perceptron, MLP). These models were utilized to predict potential patients of COVID-19 based on their signs and symptoms. The MLP has shown the best accuracy (91.62%) compared to the other models. Meanwhile, the SVM has shown the best precision 91.67%. © 2020 Science and Information Organization.

引用

页码：122 / 126

页数：4

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