  Machine Learning (ML) has garnered considerable attention from researchers
and practitioners as a new and adaptable tool for disease diagnosis. With the
advancement of ML and the proliferation of papers and research in this field, a
complete examination of Machine Learning-Based Disease Diagnosis (MLBDD) is
required. From a bibliometrics standpoint, this article comprehensively studies
MLBDD papers from 2012 to 2021. Consequently, with particular keywords, 1710
papers with associate information have been extracted from the Scopus and Web
of Science (WOS) database and integrated into the excel datasheet for further
analysis. First, we examine the publication structures based on yearly
publications and the most productive countries/regions, institutions, and
authors. Second, the co-citation networks of countries/regions, institutions,
authors, and articles are visualized using R-studio software. They are further
examined in terms of citation structure and the most influential ones. This
article gives an overview of MLBDD for researchers interested in the subject
and conducts a thorough and complete study of MLBDD for those interested in
conducting more research in this field.
