Prediction of COVID-19 using Machine Learning Techniques

Background: Over the past 4-5 months, the Coronavirus has rapidly spread to all parts of the world. Research is continuing to find a cure for this disease while there is no exact reason for this outbreak. As the number of cases to test for Coronavirus is increasing rapidly day by day, it is impossible to test due to the time and cost factors. Over recent years, machine learning has turned very reliable in the medical field. Using machine learning to predict COVID-19 in patients will reduce the time delay for the results of the medical tests and modulate health workers to give proper medical treatment to them. Objectives: The main goal of this thesis is to develop a machine learning model that could predict whether a patient is suffering from COVID-19. To develop such a model, a literature study alongside an experiment is set to identify a suitable algorithm. To assess the features that impact the prediction model. Methods: A Systematic Literature Review is performed to identify the most suitable algorithms for the prediction model. Then through the findings of the literature study, an experimental model is developed for prediction of COVID-19 and to identify the features that impact the model. Results: A set of algorithms were identified from the Literature study that includes SVM (Support Vector Machines), RF (Random Forests), ANN (Artificial Neural Network), which are suitable for prediction. Performance evaluation is conducted between the chosen algorithms to identify the technique with the highest accuracy. Feature importance values are generated to identify their impact on the prediction. Conclusions: Prediction of COVID-19 by using Machine Learning could help increase the speed of disease identification resulting in reduced mortality rate. Analyzing the results obtained from experiments, Random Forest (RF) was identified to perform better compared to other algorithms