Based on the World Health Organization, cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. Globally, about 1 in 6 deaths is due to cancer, and approximately 70% of deaths from cancer occur in low and middle-income countries. with accelerating developments in technologies and the digitization of healthcare, a lot of cancer's data have been collected, and multiple cancer repositories have been created as a result. cancer has become a data-intensive area of research over the last decade. A large number of researchers have used data mining algorithms in predicting different types of cancer to reduce the cost of tests used to predict different types of cancer, especially in low and middle-income countries. This paper reports on a systematic examination of the literature on data mining algorithms predicting different types of cancer through which we provide a thorough review, analysis, and synthesis of research published in the past 10 years. We follow the systematic literature review methodology to examine theories, problems, methodologies, and major findings of related studies on data mining algorithms predicting cancer that were published between 2009 and 2019. Using thematic analysis, we develop a research taxonomy that summarizes the main algorithms used in the existing research in the field, and we identify the most used data mining algorithms in predicting different types of cancer. In addition, to data mining algorithms used in predicting each type of cancer, as mentioned in the reviewed studies. We also identify the most popular types of cancer that researchers tackled using predictive analytics.
Al-Aiad, Ahmad; Abualrub, Salsabil; Alnsour, Yazan; and Alsharo, Mohammad, "Data Mining Algorithms Predicting Different Types of Cancer: Integrative Literature Review" (2020). AMCIS 2020 TREOs. 59.
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