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Applications of Machine Learning Models With Medical Images and Omics Technologies in Diabetes Detection
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Author(s): Chakresh Kumar Jain (Jaypee Institute of Information Technology, India), Aishani Kulshreshtha (Jaypee Institute of Information Technology, India), Avinav Agarwal (Jaypee Institute of Information Technology, India), Harshita Saxena (Jaypee Institute of Information Technology, India), Pankaj Kumar Tripathi (Jaypee Institute of Information Technology, India)and Prashant Kaushik (Jaypee Institute of Information Technology, India)
Copyright: 2024
Pages: 26
Source title:
Research Anthology on Bioinformatics, Genomics, and Computational Biology
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/979-8-3693-3026-5.ch013
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Abstract
Diabetes mellitus is a long-term condition characterized by hyperglycaemia resulting in the emergence of a variety of health problems, such as diabetic retinopathy, kidney failure, dental problems, heart disease, nerve damage, etc.; and is governed by several factors, i.e. biological, genetics, food habits, sedentary lifestyle choices, poor diets and environments, etc. According to the recent morbidity figures, the global diabetic patient population is anticipated to reach 642 million by 2040, implying that one out of every ten people will be diabetic. The data generation and AI based methods—i.e., SVM, kNN, decision tree, Baysian method in medical health –have facilitated the effective prediction and classification of voluminous size of biological data of different types of BMI, skin thickness, glucose, age, tongue and retinal images apart from Omics data, for early diagnostics. The chapter summarizes the basic methods and applications of machine learning and soft computing techniques for diabetes diagnosis and prediction with limitations of integrative approaches.
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