Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches

Abstract

COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

A Hybrid Model with Spherical Fuzzy-AHP, PLS-SEM and ANN to Predict Vaccination Intention against COVID-19

“…From existing literature review, a number of mathematical and statistical models have been constructed to conduct a critical analysis of the continuing COVID-19 and other related disease outbreaks [26][27][28]. Alyasseri et al [26] conducted a thorough assessment of prior research that used deep learning (DL) and machine learning (ML) approaches to provide quick and accurate COVID-19 diagnosis in the healthcare sector, and their findings revealed that ANN was the most commonly used DL mechanism. Sabzi et al [27] also proposed that time series forecasting models be used with the MCDM model to offer more dependable and accurate predictions.…”

Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images

“…Alyasseri et al [ 19 ] reviewed comprehensive work on COVID-19 using deep learning and machine learning. They summarized previous studies on COVID-19 and concluded that CNN primarily uses a deep learning algorithm.…”

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

“…The proposed model achieved a correlation of 0.9763 correlation on China's COVID-19 outbreak data. For more related works, we would like to refer the readers to the review papers [35,36].…”