A Comprehensive Review of COVID-19 Detection and Prediction Using of ML/DL Method

COVID-19, a new coronavirus, causes severe acute respiratory syndrome (SARS-CoV-2). As of late, COVID-19 spread to more than 500 million individuals throughout 230 nations and territories, turning it into a pandemic. Traditional diagnosis methods are no longer effective due to the exponential rise in infection rates Machine learning (ML) and Deep learning (DL), two intelligence techniques that have been developed by numerous researchers, can assist the healthcare industry in providing prompt and accurate COVID-19 detection. As a result, this work offers an in-depth analysis most recently ML and DL methods for COVID-19 analysis. In-depth investigations are released between December 2019 and November 2022. Generally speaking, this document contains more than 200 studies that were carefully chosen from a variety of publications, including MPDI, ScienceDirect, Springer, Elsevier, and IEEE. For identifying COVID-19 and forecasting outbreaks, SVM is the most widely used machine learning method, and CNN is the most widely used deep learning algorithm. The most often utilized metrics in prior investigations were accuracy, sensitivity, and specificity. The research community will receive guidance from this review paper regarding the expected development of machine learning and deep learning for COVID-19 and motivation for their prospective work.