Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia

Monitoring child development is vital in Indonesia due to its large child population and varying socio-economic and geographical conditions. Malnutrition adversely affects children's growth and development, with ongoing challenges in remote areas despite government efforts. This study addresses...

Full description

Saved in:
Bibliographic Details
Published in:BIO web of conferences 2024-01, Vol.146, p.01082
Main Authors: Syakur Muhammad Ali, Putra Adz Dzikry Pradana, Rochman Eka Mala Sari, Mufarrohah Fifin Ayu, Husni, Asmara Yuli Panca, Rachmad Aeri
Format: Article
Language:English
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Monitoring child development is vital in Indonesia due to its large child population and varying socio-economic and geographical conditions. Malnutrition adversely affects children's growth and development, with ongoing challenges in remote areas despite government efforts. This study addresses the need for accurate nutritional status classification to improve intervention strategies. This study applies the Support Vector Machine (SVM) classification method to analyze and classify nutritional status of toddlers using data from 473 samples collected from health centers in Bangkalan Regency. The classification includes categories such as Good Nutrition, Excess Nutrition, Obesity, and Risk of Excess Nutrition. The SVM model achieved an accuracy of 76% in predicting nutritional status.
ISSN:2117-4458
DOI:10.1051/bioconf/202414601082