Reviewing Data Analytics Techniques in Breast Cancer Treatment

Data mining (DM) or Data Analytics is the process of extracting new valuable information from large quantities of data; it is reshaping many industries including the medical one. Its contribution to medicine is very important particularly in oncology. Breast cancer is the most common type of cancer in the world and it occurs almost entirely in women, but men can get attacked too. Researchers over the world are trying every day to improve, prevention, detection and treatment of Breast Cancer (BC) in order to provide more effective treatments to patients. In this vein, the present paper carried out a systematic map of the use of data mining technique in breast cancer treatment. The aim was to analyse and synthetize studies on DM applied to breast cancer treatment. In this regard, 44 relevant articles published between 1991 and 2019 were selected and classified according to three criteria: year and channel of publication, research type through DM contribution in BC treatment and DM techniques. Of course, there are not many articles for treatment, because the researchers have been interested in the diagnosis with the different classification techniques, and it may be because of the importance of early diagnosis to avoid danger. Results show that papers were published in different channels (especially journals or conferences), researchers follow the DM pipeline to deal with a BC treatment, the challenge is to reduce the number of non-classified patients, and affect them in the most appropriate group to follow the suitable treatment, and classification was the most used task of DM applied to BC treatment. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.