title: Machine Learning and Data Mining Methods in Diabetes Research.
abstract: The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.
authors.affiliation: Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kavakiotis
authors.affiliation: Laboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
authors.forename: Olga
authors.initials: O
authors.lastname: Tsave
authors.affiliation: Laboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
authors.forename: Athanasios
authors.initials: A
authors.lastname: Salifoglou
authors.affiliation: Institute of Applied Biosciences, CERTH, Thessaloniki, Greece; Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
authors.forename: Nicos
authors.initials: N
authors.lastname: Maglaveras
authors.affiliation: Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Vlahavas
authors.affiliation: Institute of Applied Biosciences, CERTH, Thessaloniki, Greece; Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
authors.forename: Ioanna
authors.initials: I
authors.lastname: Chouvarda
keywords: Biomarker(s) identification; Data mining; Diabetes mellitus; Diabetic complications; Disease prediction models; Machine learning