Machine Learning and Data Mining Methods in Diabetes Research.
Ioannis Kavakiotis,O. Tsave,Athanasios Salifoglou,Nicos Maglaveras,Ioannis Vlahavas,Ioanna Chouvarda +5 more
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TLDR
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.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.read more
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References
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Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis
TL;DR: The application of data analytics to medical records from the Diabetes Screening programme demonstrates that data analytics, combined with large clinical datasets can be used to identify clinically appropriate cut-offs values and identify novel biomarkers that when included improve the accuracy of T2DM diagnosis even when HbA1c levels are below or equal to the current cut-off of 6.5%.
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E. Halevas,O. Tsave,Maria P. Yavropoulou,Antonios G. Hatzidimitriou,John G. Yovos,V. Psycharis,C. Gabriel,Athanasios Salifoglou +7 more
TL;DR: Insight is gained into vanadium's potential as a future metallodrug in Diabetes mellitus II by influencing the emergence of its (a)toxicity and for the first time its insulin-like activity in pre-adipocyte differentiation.
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Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy
TL;DR: Several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS, a real-time clinical decision support system with data stream mining.
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A flexible data-driven comorbidity feature extraction framework
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Realization of a service for the long-term risk assessment of diabetes-related complications
Vincenzo Lagani,Franco Chiarugi,Dimitris Manousos,Vivek Verma,Joanna Fursse,Kostas Marias,Ioannis Tsamardinos,Ioannis Tsamardinos +7 more
TL;DR: This work provides a working example of risk-stratification tool that is specific for diabetes patients, able to handle several different diabetes related complications, and performing as well as the widely known UKPDS Risk Engine on an external validation cohort.