Journal of Intelligent & Fuzzy Systems

Early diagnosis of diabetes mellitus using data mining and machine learning techniques

Publication Date Oct 18, 2022


The remarkable developments in biotechnology as well as the health sciences have resulted in the production of an enormous amount of data, including high-throughput screening genomics information and clinical information obtained through extensive electronic health records (EHRs). The application of data mining and machine learning techniques in the biosciences is today more vital than ever to achieving this objective as attempts are made to intelligently translate all readily available data into knowledge. Diabetes mellitus (DM), a group of metabolic disorders, is well known to have a serious detrimental effect on population lives all over the world. Large-scale research into all aspects of diabetic has resulted in the production of enormous amounts of data (detection, etiopathophysiology, therapy, etc.). The goal of the current study is to conduct a thorough examination of the use of machine learning, data mining methods and tools in the field of diabetes research, with the first classification making an appearance to be the most popular. These applications relate to a Statistical model and Diagnosis, b) Diabetic Complications, c) Multiple genes Background and Environment, and e) Free Healthcare and Management. Numerous machine learning algorithms were applied. 85% of the methods used were supervised learning approaches, whereas 15% were uncontrolled ones, including association rules. Developed on improved support vector machines, the most successful and widely used algorithm (SVM). Medical datasets were predominantly used...


Early Diagnosis Of Diabetes Mellitus Improved Support Vector Machines Machine Learning Techniques Application Of Data Mining Developments In Biotechnology Data Mining Data Mining Methods Data Mining Techniques Machine Learning Medical Datasets

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