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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Journal ArticleDOI
Development of a Clinical Forecasting Model to Predict Comorbid Depression Among Diabetes Patients and an Application in Depression Screening Policy Making.
TL;DR: The depression prediction model developed in this study has compelling predictive ability and can be adopted by health care providers to use their resources and time better and increase their efficiency in managing their patients with depression.
Book
Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data
Mourad Elloumi,Albert Y. Zomaya +1 more
TL;DR: This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining, providing in-depth fundamental and technical field information on the most important topics encountered.
Proceedings Article
Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.
Omolola Ogunyemi,Dulcie Kermah +1 more
TL;DR: Successful approaches to detecting latent retinopathy using machine learning could help safety net and other clinics identify unscreened patients who are most at risk of developing Retinopathy and the use of ensemble classifiers on clinical data shows promise for this purpose.
Journal ArticleDOI
An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database.
TL;DR: The study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes.
Journal ArticleDOI
Detecting intentional insulin omission for weight loss in girls with type 1 diabetes mellitus
Orit Pinhas-Hamiel,Orit Pinhas-Hamiel,Orit Pinhas-Hamiel,Uri Hamiel,Yuval Greenfield,Valentina Boyko,Chana Graph-Barel,Marianna Rachmiel,Liat Lerner-Geva,Brian Reichman +9 more
TL;DR: A clinical prediction model is developed for the detection of intentional insulin omission for weight loss in adolescent females with type 1 diabetes mellitus and shows good discrimination and sensitivity and specificity.