Systematic Map of Data Mining For Gynecologic Oncology

Gynecologic cancers are a significant cause of morbidity and mortality among women both in developed and low- middle- income countries. To al-leviate the burden, the application of data mining in gynecologic oncology is needed in clinical environments. This study presents a systematic mapping to explore in detail the breadth of the available literature on the use of Data Mining (DM) in gynecologic oncology. The mapping questions and the PICO framework served to determine the search string of this systematic map. The resultant was conducted on five well-known databases, PubMed, IEEE Xplore, ScienceDirect, Springer Link, and Google Scholar, to catch rel-evant articles published between 2011, and mid of 2021. Of the 2,807 poten-tial records, 115 studies fulfilled the inclusion/exclusion criteria and were in-depth analyzed. The findings revealed that DM efforts peaked considerably from 2019 in terms of cervical cancer screening and diagnosis. Further stud-ies are needed to investigate a wider range of research questions as gyneco-logic oncology is a very rich field with a collection of distinct features can-cers which, in turn, allow ML opportunities.