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ISSN Number:
2582-8568


Journal DOI No:
03.2021-11278686

Title:
Diagnosing Malnutrition in Preschool Children Using Machine Learning Approach: A Review

Authors:
Mr. Amol Avinash Shinde , Dr. D. V. Sahasrabuddhe

Cite this Article:
Mr. Amol Avinash Shinde , Dr. D. V. Sahasrabuddhe ,
Diagnosing Malnutrition in Preschool Children Using Machine Learning Approach: A Review,
International Research Journal of Humanities and Interdisciplinary Studies (www.irjhis.com), ISSN : 2582-8568, Special Issue, January 2024 International Conference Organized by V. P. Institute of Management Studies & Research, Sangli (Maharashtra, India), Page No : 26-35,
Available at : http://irjhis.com/paper/IRJHISIC2401004.pdf

Abstract:

Child’s health is not only the concern of parent but also it is priority of government of every nation to build a strong country. The child may face different health related problems during the preschool ageas they are being exposed to the external environment [ (Khare, 2017)(22), (Ula M. U., 2021)(30)]. Major health issues are due to lack nutritional diet which in turn tends to malnutrition. Different health indicators help in finding malnutrition in a child. Depending on symptoms and considering health of a child malnutrition can be treated. The most challenging task for parents is to find symptoms of malnutrition in their child prior so that precautionary measures can be taken. And here the role of technology can be observed. The machine learning technology has already influenced healthcare industry and is being used by medical professionals in managing clinical data, processing it, early diagnosis of findings.



Keywords:

Machine Learning, Healthcare, Malnutrition Diet, Nutrition.



Publication Details:
Published Paper ID: IRJHISIC2401004
Registration ID: 21284
Published In: Special Issue, January 2024 International Conference Organized by V. P. Institute of Management Studies & Research, Sangli (Maharashtra, India)
Page No: 26-35
ISSN Number: 2582-8568

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ISSN Number

ISSN 2582-8568

Impact Factor

5.71 (2021)

DOI Member


03.2021-11278686