Machine Learning and Data Mining Methods in Diabetes Research
Aristotle University of Thessaloniki · Centre for Research and Technology Hellas
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…
Citation impact
- FWCI
- 203.85
- Percentile
- 100%
- References
- 168
Authors
6- IKIoannis KavakiotisCorresponding
Aristotle University of Thessaloniki, Centre for Research and Technology Hellas
- OTO. Tsave
Aristotle University of Thessaloniki
- ASAthanasios Salifoglou
Aristotle University of Thessaloniki
- NMNicos Maglaveras
Aristotle University of Thessaloniki, Centre for Research and Technology Hellas
- IVIoannis Vlahavas
Aristotle University of Thessaloniki
Topics & keywords
- Machine learning
- Support vector machine
- Artificial intelligence
- Computer science
- Field (mathematics)
- Data science
- Knowledge extraction
- Data mining