A web application designed to predict high-risk patients affected by COVID-19 runs a machine learning model at the backend to generate results. The random forest classification technique is used to predict the high-risk status of patients who are COVID-19 positive and are at the initial stage of infection. We used hybridized algorithms to predict high-risk patients, and the model used the patients’ current underlying health conditions, such as age, sex, diabetes, asthma, hypertension, smoking, and other factors. After data preprocessing and training, the model could predict the severity of the patient with an accuracy of 65-70%. According to some studies, random forest ML models outperform other ML models for solving the challenge of predicting unusual events, such as in this case. Pneumonia, hypertension, diabetes, obesity, and chronic renal disease were the most contributory variables for model implementation. This project will help patients and hospital staff make necessary decisions and actions in advance. This will help healthcare workers arrange resources and hospital areas for high-risk COVID-19 patients. Thus, this study provides an effective and optimized treatment. Using this application and suitable patient data, hospitals can predict whether a patient will require urgent care.

Due the quick spread of coronavirus disease 2019 , identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.

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