مقاله انگلیسی رایگان در مورد یادگیری ماشین و روش داده کاوی در تحقیقات دیابت – الزویر 2017

Significant advances in biotechnology and more specifically highthroughput sequencing result incessantly in an easy and inexpensive data production, thereby ushering the science of applied biology into the area of big data [1,2]. To date, besides high performance sequencing methods, there is a plethora of digital machines and sensors from various research fields generating data, including super-resolution digital microscopy, mass spectrometry, Magnetic Resonance Imagery (MRI), etc. Although these technologies produce a wealth of data, they do not provide any kind of analysis, interpretation or extraction of knowledge. To this end, the area of Biological Data Mining or otherwise Knowledge Discovery in Biological Data, is more than ever necessary and important. The primary objective is to delve into the rapidly accruing body of biological data and set the basis potentiating answers to fundamental questions in biology and medicine. The power and effectiveness of these approaches are derived from the ability of commensurate methods to extract patterns and create models from data. The aforementioned fact is particularly significant in the big data era, especially when the dataset can reach terabytes or petabytes of data. Consequently, the abundance of data has strengthened considerably data-oriented research in biology. In such a hybrid field, one of the most important research applications is prognosis and diagnosis related to human-threatening and/or life quality reducing diseases. One such disease is diabetes mellitus (DM). Applying machine learning and data mining methods in DM research is a key approach to utilizing large volumes of available diabetes-related data for extracting knowledge. The severe social impact of the specific disease renders DM one of the main priorities in medical science research, which inevitably generates huge amounts of data. Undoubtedly, therefore, machine learning and data mining approaches in DM are of great concern when it comes to diagnosis, management and other related clinical administration aspects. Hence, in the framework of this study, efforts were made to review the current literature on machine learning and data mining approaches in diabetes research.