• Kalpesh Bhoi
• Gaurav Patil
• Shubham Patil
• Kunal Patil
• Santosh Bhandare

The prevalence of obesity is rising quickly across a range of demographics, making it a serious worldwide health concern that places a significant financial burden on both individuals and healthcare systems. The goal of this research is to improve early intervention and tailored healthcare methods by investigating the use of machine learning techniques for predicting the risk of obesity. Through the utilization of large-scale information and sophisticated algorithms, machine learning presents a promising avenue for identifying intricate patterns and predictors of obesity that may remain hidden by conventional techniques. The methods, difficulties, and possible ramifications of applying machine learning to the prediction of obesity risk are described in this article. It emphasizes how crucial it is to identify people who are at-risk early in order to provide focused therapies and stop problems from obesity. The study looks into a variety of machine learning techniques, such as neural networks, decision trees, random forests, and support vector machines.

Obesity, Global epidemic, Health Care, Obesity level detection, Supervised machine learning, Feature Selection, Hyper-parameter tuning.

"Obesity Disease Risk Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.a120-a124, June-2024, Available :http://www.jetir.org/papers/JETIR2406016.pdf

"Obesity Disease Risk Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppa120-a124, June-2024, Available at : http://www.jetir.org/papers/JETIR2406016.pdf