The Role of Machine Learning to Fight COVID-19

Objectives: this paper aimed to first, exploring the relationship between multiple physical measurements.Second, predicting the treatment course of hospitalized patients with COVID-19 disease from physical measurements.Third, investigating the primary symptoms and the average duration of each symptom's disappearance.Fourth, provide the physicians with the prediction model to help them determine the best combination of drugs for Covid-19 patients'.Methods: this paper first, apply correlation analysis on the dataset to identify the relationships between the dataset attributes and selecting model features more efficiently to improve the accuracy results.Second, implements seven machine learning algorithms with four cross-validation techniques to predict the most appropriate treatment course for COVID-19 hospitalized patients.Two treatment courses were identified; course-LR was patients treated with Lopinavir-ritonavir and course LR + AR were patients treated with Lopinavir-ritonavir combined with arbidol for antiviral treatment.Results: by applying correlation analysis between dataset attributes, we found that there is no relationship between the presence of chronic diseases or the patient' age and the Covid-19 clinical classification.The prediction model results show that 10 fold cross-validation with Naïve Bayes and neural network achieving the highest accuracy of 85.71%.Conclusion: this paper has exploited correlation analysis and machine learning based approaches to identify relevant attributes in the COVID-19 patients' dataset and predicting the most appropriate treatment course.