Machine Learning and Data Mining Methods in Diabetes Research

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 bio...

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Veröffentlicht in:Computational and structural biotechnology journal Jg. 15; S. 104 - 116
Hauptverfasser: Kavakiotis, Ioannis, Tsave, Olga, Salifoglou, Athanasios, Maglaveras, Nicos, Vlahavas, Ioannis, Chouvarda, Ioanna
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.01.2017
Research Network of Computational and Structural Biotechnology
Elsevier
Schlagworte:
Biomarker(s) identification
biotechnology
data collection
Data mining
Diabetes mellitus
Diabetic complications
Disease prediction models
genetic background
health services
human health
Machine learning
prediction
Review
support vector machines
systematic review
therapeutics
Diabetic complications
Data mining
Disease prediction models
Diabetes mellitus
Machine learning
Biomarker(s) identification
ISSN:2001-0370, 2001-0370
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Zusammenfassung: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.
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ISSN:2001-0370
2001-0370
DOI:10.1016/j.csbj.2016.12.005