Prediction of COVID-19 Severity by Applying Machine Learning Techniques

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Abstract: An intimidating spread of COVID- 19(which is also known as severe acute respiratory pattern coronavirus 2 or SARSCOV- 2) led scientists to conduct tremendous sweats to reduce the epidemic goods. Artificial intelligence (AI) methods that are quick and precise are needed to support croakers in their assessments of a case's inflexibility and mortality hazard. By providing early medicine administration, pre-vaccination of rigid patients will reduce the cost to the hospital and stop instances from dying continuously. This design uses machine learning and deep learning techniques to build a vaticination model that predicts several inflexibility problems for the COVID-19 case grounded on X-ray pictures. Non-Handcrafted styles and composite handcrafted ways are applied to extract features. Principal Component Analysis( PCA) is being incorporated to choose the most vital features, and also Machine and Deep learning ways are applied. To extract characteristics, non-handmade styles and composite handcrafted methods are used. To choose the most crucial features, Principal Component Analysis (PCA) is used with machine and deep learning techniques. The cases are categorised and classified as pneumonia, severe, or normal. The PCA features enabled the Bagging, Ada Boost, KNN (K- nearest neighbors), and XGBoost classifiers to perform stylishly with 97% of accuracy, 98% of precision, and recall of 95%. This study proposes a new prophetic frame for the inflexibility and mortality threat of COVID-19 cases to help hospitals, croakers, and medical installations in their decision-making about which cases need to get attention first before others, and at the same time, to keep hospitals ’ coffers for high- threat precedence cases.