Open AccessA Review of Diabetes Mellitus Detection using Machine Learning Techniques
Author(s) -
R Kumar,
S. Pazhanirajan
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f8748.0410621
Subject(s) - artificial intelligence , computer science , diabetes mellitus , identification (biology) , machine learning , process (computing) , field (mathematics) , medicine , botany , mathematics , pure mathematics , biology , endocrinology , operating system
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.