Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases
It addresses the need for early detection of infectious diseases to prevent epidemics, but it is incremental as it reviews existing research without introducing new methods.
This paper reviewed recent machine-learning algorithms for diagnosing infectious diseases, finding that most studies used small datasets and few employed real-time data, and concluded that the choice of ML technique depends on the dataset and goal.
Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal.