A Review of Diabetes Mellitus Detection using Machine Learning Techniques
Kumar R1, S Pazhanirajan2

1Kumar R*, Research Scholar, Department of CSE, Annamalai University, Chidambaram, (Tamil Nadu), Assistant Professor, MVJ College of Engineering, Bangalore, India.
2Dr. S Pazhanirajan, Assistant Professor, Department of CSE, Annamalai University, Chidambaram, (Tamil Nadu), India.

Manuscript received on April 07, 2021. | Revised Manuscript received on April 13, 2021. | Manuscript published on April 30, 2021. | PP: 32-41 | Volume-10 Issue-6, April 2021 | Retrieval Number: 100.1/ijitee.F87480410621| DOI: 10.35940/ijitee.F8748.0410621
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Diabetes Mellitus (DM) is a disease that can lead to a multi-organ malfunctioning in patients due to non-regulated diabetes. Recent advancements in machine learning (ML) and artificial intelligence, the early detection and diagnosis of DM is more advantageous than the manual diagnosis through an automated process. It this review, DM’s recognition, diagnosis and self-management techniques from six facets, namely DM datasets, techniques involved in pre-processing, extraction of features; identification through ML; classification and diagnosis of DM; intelligent DM assistant based on artificial intelligence; are thoroughly analyzed and presented. The findings of the previous research and their inferences are interpreted. This analysis also offers a comprehensive overview of DM detection and self-administration technologies that can be of use to the research community working in the field of automated DM detection and self-management. 
Keywords: Diabetes Mellitus; machine learning; detection; classification; prediction; algorithms.