Authors :
Leelavathi Arepalli; Durga Sasindra Vakalapudi; Sai Nivesh Bomma; Gowthami Maka; Pavan Kalyan Saila; Nitya Sri Nekkanti
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/4tpu8ev4
Scribd :
http://tinyurl.com/4zpthv42
DOI :
https://doi.org/10.5281/zenodo.10441145
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.
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.
