Classification for Covid-19 Diseases Based on Ensembled Models

Abstract

After Covid19 became a worldwide health issue, rapid diagnosis based on clinical symptoms from many diseases with similar symptoms to Covid19 became important to slow down the spread of the pandemic. This study attempts to find ways to classify and diagnose diseases with the help of computer technology quickly and accurately. In this study, the author developed an ensembled machine learning model to categorize four diseases using information on their distinct clinical signs. The authors used a Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based ensemble model. To improve the accuracy of the classification result, this model adds the strong classifier SVM to the result of an intermediate hidden layer of the fundamental ANN deep learning model. The result of the study shows that the integrated model's prediction performance is significantly better than that of the original ANN model after the support of another strong classification algorithm. In conclusion, the effectiveness of the proposed method was proved for classifying the symptoms of patients with allergies, colds, flu, and Covid-19 in this study.

HAR-BiNet: COVID-19 Prediction Using Hybrid Attention based Residual Bidirectional Gated Recurrent Unit

“…More accurately than RT-PCR, X-rays or CT scans can detect COVID-19 by considering the symptoms in the lower regions of the lungs [8]. RT-PCR testing can occasionally be used in place ofX-ray and CT scans examinations.…”