Systematic Contemplate Paradigm on Diabetes Mellitus using different Machine Learning Predictive Techniques

Title

Systematic Contemplate Paradigm on Diabetes Mellitus using different Machine Learning Predictive Techniques

Creator

Padmaja K.; Mukhopadhyay D.

Description

As the foodies love fast food, from micro to combined families across the world the ratio of family members 1:4 is affected with silent killer named as diabetes. A very high blood glucose levels, metabolism, improper carbohydrate, damaged hormone insulin alleviating a human body disability leading to the silent killer of the body parts is the diabetes. An estimated 425 million of people around the globe suffering with diabetes up to 108 million to 1.7 trillion will be affected with diabetes. Therefore millennium, the universe ubiquity suffering with diabetes has next to quadrupled, growing from 9 percent and above among the people. As the eating habits of people in this trendy 21st century is dramatically devastating to the risk of overweight or obese. The silent killer diabetes consequences include kidney failure, Diabetic retinopathy, Heart attack, Stiffness of body muscles, Nerves stroke and lower limb amputation leads to type I and type II diabetes. As the researchers across the globe are using the machine learning algorithms as the reliable problem solver, The complications still continue. The purpose of this percu is to help with the apt selection of features garnishing with machine learning paradigm techniques in selecting the accurate attributes for each person to be properly diagnosed. In this archetype survey paper, we have done a systematic review chronologically a decade research which will help the researchers to explore and get the contemplate on various tangible and intangible data sets they can adopt in diagnosing the mellitus diabetes. Grenze Scientific Society, 2023.

Source

14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023, Vol-2023-June, pp. 1768-1775.

Date

2023-01-01

Publisher

Grenze Scientific Society

Subject

data set; Diabetes Mellitus; Machine Learning; Supervised; support vector machine; Unsupervised

Coverage

Padmaja K., CHRIST (Deemed to be University), Department of CSE, Bangalore, India; Mukhopadhyay D., CHRIST (Deemed to be University), Department of CSE, Bangalore, India

Rights

Restricted Access

Relation

0

Format

Online

Language

English

Type

Conference paper

Identifier

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174401839&partnerID=40&md5=1d81b6aed8b44e953177760b4ca071bb