  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.
