  Globally, there is a substantial unmet need to diagnose various diseases
effectively. The complexity of the different disease mechanisms and underlying
symptoms of the patient population presents massive challenges to developing
the early diagnosis tool and effective treatment. Machine Learning (ML), an
area of Artificial Intelligence (AI), enables researchers, physicians, and
patients to solve some of these issues. Based on relevant research, this review
explains how Machine Learning (ML) and Deep Learning (DL) are being used to
help in the early identification of numerous diseases. To begin, a bibliometric
study of the publication is given using data from the Scopus and Web of Science
(WOS) databases. The bibliometric study of 1216 publications was undertaken to
determine the most prolific authors, nations, organizations, and most cited
articles. The review then summarizes the most recent trends and approaches in
Machine Learning-based Disease Diagnosis (MLBDD), considering the following
factors: algorithm, disease types, data type, application, and evaluation
metrics. Finally, the paper highlights key results and provides insight into
future trends and opportunities in the MLBDD area.
