Systematic map and review of predictive techniques in diabetes self-management

Data mining (DM) provides powerful tools to extract knowledge from large volumes of data offering valuable information to decision making. The extracted knowledge can be used for predictive and/or descriptive purposes. DM has been successfully used in different subfields of eHealth such us cardiology, oncology and endocrinology. This paper deals with the use of DM predictive techniques in diabetes self-management (DSM).To the best of our knowledge, neither a systematic map nor a systematic review have yet been performed with a focus on the use of DM predictive techniques in DSM. Thus, the aim of this study is to classify and review primary studies investigating DM predictive techniques in DSM by summarizing and analyzing knowledge with respect to: year and source of publication, type of diabetes, clinical tasks, DM predictive techniques, and the performance of the predictive techniques used. A total of 38 papers published between 2000 and April 2017 were therefore selected and analyzed accordingly to address six research questions. The results show that Type 1 Diabetes Mellitus (T1DM) is largely the type of diabetes that is most concerned by the studies and the prediction of blood glucose is the most investigated clinical task. Moreover, artificial neural networks were the most frequently used predictive technique which along with autogressive models, yield highest accuracy rates.