International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
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Volume 7 Issue 2
March-April 2025

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Leveraging Machine Learning Techniques to Effectively Predict Malnutrition among Children

Author(s) Prof. Ms. Reshma Tushar Ladda, Babasaheb Sonawane
Country India
Abstract The objective of the research is to predict malnutrition in children through the application of different algorithms in machine learning. The lack of adequate muscle and fat tissue development during intrauterine growth is a hallmark of malnutrition. It is mostly brought on by inadequate nourishment for the mother and placental insufficiency, which results in lower rates of infant morbidity and mortality across the world. This study focuses on to determine whether malnutrition is present in children by calculating their Z-score, which takes into consideration age in months, weight, peak, and sex. The dataset that was used in this project was obtained for network education from UNICEF. The given dataset is divided into two parts: one for verification and the other for analysis. We identify infant malnutrition by calculating WAZ (underweight) and LAZ (stunting), and we train the fashions to do so. Several machine learning techniques, such as logistic regression, KNN, and Naïve Bayes, are used to identify childhood malnutrition. Out of all these designs, logistic regression shows better accuracy than the other algorithms.
Keywords Malnutrition, WAZ (Weight for age Z score) or underweight, HAZ (Height for age Z score) or stunting, ML (Machine Learning), KNN, Logistic Regression, Naïve Bayes Algorithm
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-27
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.39992
Short DOI https://doi.org/g8934x
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E-ISSN 2582-2160
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