International Journal of Advances in Applied Computational Intelligence
Journal DOI
2833-5600ISSN (Online)
Volume 3 , Issue 1 , PP: 08-18, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mona Mohamed 1 *
Doi: https://doi.org/10.54216/IJAACI.030101
One of the major lifestyle disorders brought on by unwholesome daily routines and inherited ailments is obesity and overweight. And this illness is a risk factor for a wide range of chronic illnesses, such as cancer, diabetes, metabolic syndrome, and cardiovascular conditions. Additionally, according to the World Health Organization (WHO), 30% of deaths worldwide will be caused by lifestyle illnesses by 2030. These deaths can be prevented by appropriately identifying and treating risk factors that relate to these diseases as well as by implementing behavioral engagement policies. Thence, the study is leveraging machine learning (ML) techniques for analyzing data and discovering new patterns for predicting body fat. The problem of predicting fat classifies as a regression, hence, we are deploying two regression techniques to deal with the regression dataset. These techniques are used linear regression (LR) and k nearest neighbors (KNN) which fall under umbral of ML. The two techniques are applied on real datasets. The dataset has 252 records. The results showed the LR has the highest score than the KNN model.
Machine Learning , Linear Regression , K Nearest Neighbors , Body Fat , Prediction , Regression Problem , obesity.
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