Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population
This work addresses the need for early prognostication to aid resource allocation during pandemics, specifically for the Bangladeshi population, but it is incremental as it applies existing methods to new data.
This study tackled the problem of predicting COVID-19 infection and death rates in Bangladesh using machine learning models, with results indicating that a multi-layer perceptron (MLP) model outperformed others and predicted future cases ranging from 929 to 2443 infections and 19 to 57 deaths.
During a pandemic, early prognostication of patient infected rates can reduce the death by ensuring treatment facility and proper resource allocation. In recent months, the number of death and infected rates has increased more distinguished than before in Bangladesh. The country is struggling to provide moderate medical treatment to many patients. This study distinguishes machine learning models and creates a prediction system to anticipate the infected and death rate for the coming days. Equipping a dataset with data from March 1, 2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. The data was managed from a trusted government website and concocted manually for training purposes. Several test cases determine the model's accuracy and prediction capability. The comparison between specific models assumes that the MLP model has more reliable prediction capability than the support vector regression (SVR) and linear regression model. The model presents a report about the risky situation and impending coronavirus disease (COVID-19) attack. According to the prediction produced by the model, Bangladesh may suffer another COVID-19 attack, where the number of infected cases can be between 929 to 2443 and death cases between 19 to 57.