Comparison to the Proposed Hybrid Model and Machine Learning Techniques for Survival Prediction of Corona, Infected Patients

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Md. Asadullah
Md. Murad Hossain
Md. Matiur Rahman Molla
Md. Matiur Rahaman

Abstract

SARS-CoV-2, a novel coronavirus discovered in Wuhan, China is spreading quickly and has a high incidence rate around the globe. As a result, everyone on the planet is having difficulty adjusting to the effects of Corona and is unable to foresee the devastation and disaster caused by COVID-19. In this work, we predict the survival status of patients infected with coronavirus using three distinct machine learning (ML) techniques: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). We also assess the classification performances of these algorithms. Here, we put out a hybrid model and evaluated it against the three previously discussed machine learning techniques. The outcomes demonstrated the 97.85% prediction accuracy of our suggested hybrid model. Aside from our suggested hybrid model, the random forest machine learning method demonstrated the highest accuracy of 94.62% among the three. Nonetheless, the prediction accuracy of the hybrid model outperforms that of the random forest and is significantly better than that of the other three ML techniques. The classification performances were assessed using the F-score, sensitivity, specificity, and precision metrics. Using 10-fold cross-validation, ROC assessments and confusion matrices produced by these machine learning algorithms were provided and examined. to assess the effectiveness of the classification. These machine learning algorithms' ROC assessments and confusion matrices are shown and examined by 10-fold cross-validation.

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How to Cite
Asadullah, M., Hossain, M. M., Molla, M. M. R., & Rahaman, M. M. (2023). Comparison to the Proposed Hybrid Model and Machine Learning Techniques for Survival Prediction of Corona, Infected Patients. Advances in Systems Science and Applications, 23(04), 148-155. https://doi.org/10.25728/assa.2023.23.04.1115
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