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
Online-Zugang:	Volltext
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