Publication Date: 2023/12/29
Abstract: The main theme of our paper is to early detection of vitamin-A deficiency in school children by using Logistic Regression.[1][2] Vitamin ‘A’ Deficiency is a significant public health issue affecting millions of children worldwide [4], particularly in developing countries. It can lead to serious health consequences, including impaired vision, and weakened immunity. Early detection and classification of this deficiency in schoolchildren are crucial for implementing interventions to improve their overall health and well- being [3]. This project proposes the application of machine learning techniques, specifically logistic regression, to accurately classify the presence of deficiency in school-aged children based on relevant clinicaland demographic factors. The primary objective of this research is to develop a predictive model that can efficiently and accurately identify the children at risk of this deficiency [9]. The proposed project will utilize a dataset collected from schoolchildren in target regions, encompassing key features such as age, sex, location, and symptoms related to Vitamin A [6][9]. These data will be processed and pre-processed to ensure data quality and remove any potential bias. Logistic regression, a widely used classification algorithm in machine learning, will be employed to build the predictive model. The model will be trained on a labeled subset of the dataset, where the presence or absence of Vitamin A deficiency is indicated.
Keywords: Machine Learning, Logistic Regression, Scikit- Learn, Jupyter Notebook,Pandas, Matplotlib.
DOI: https://doi.org/10.5281/zenodo.10441145
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23DEC262.pdf
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