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

• Published in: International journal of information management Vol. 46; pp. 263 - 277
• Main Authors: EL Idrissi, Touria, Idri, Ali, Bakkoury, Zohra
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.06.2019; Elsevier Science Ltd
• Subjects: Artificial neural networks; Cardiology; Data mining; Decision making; Diabetes; Diabetes mellitus; Endocrinology; Model accuracy; Performance prediction; Predictive techniques; Self-management; Systematic literature review; Systematic map; Self-management; Diabetes; Data mining; Predictive techniques; Systematic map; Systematic literature review
• ISSN: 0268-4012; 1873-4707
• DOI: 10.1016/j.ijinfomgt.2018.09.011

•Systematic map and review on the use of predictive techniques in diabetes self ma agement.•38 selected studies were analyzed considering six map and review questions.•This subject had a rising interest in the last decade especially for diabetes type I.•Blood glucose prediction was the most investigated task and Artificial Neural Networks were the most used technique.•ANNs and autoregressive models provided the highest accuracy. 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.