کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8408600 1545070 2017 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Machine Learning and Data Mining Methods in Diabetes Research
ترجمه فارسی عنوان
یادگیری ماشین و روش داده کاوی در تحقیقات دیابت
کلمات کلیدی
فراگیری ماشین، داده کاوی، دیابت عوارض دیابت مدل های پیش بینی بیماری، شناسایی بیومارکر (ها)،
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی بیوتکنولوژی یا زیست‌فناوری
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational and Structural Biotechnology Journal - Volume 15, 2017, Pages 104-116
نویسندگان
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