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Prognostic Investigation of Malnutrition in Infants Using Ml
¹³⁴⁵ ⁴th year, Department of ECE, BGS Institute of technology, Mandya, Karnataka, India. ²Assistant professor, Department of ECE, BGS Institute of Technology, Mandya, Karnataka, India.
Published Online: May-June 2024
Pages: 230-234
Cite this article
↗ https://www.doi.org/10.59256/ijire.20240503029
Abstract
View PDFAbstract: This study proposes a data science machine learning approach to predict malnutrition status in children under five years old, a significant health issue affecting a country's economic growth. Utilizing training datasets from Kaggle, hidden factors are extracted using machine learning techniques, and classification algorithms such as Bayesian classifier and K-nearest neighbor are employed for prediction. The system is built as a real-time application using Visual Studio as the front-end technology and SQL Server as the back-end technology, demonstrating the accuracy of data science classification techniques in predicting malnutrition status based on clinical datasets.
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