Paper Contents
Abstract
Millions of individuals worldwide suffer from obesity, which has become a serious health concern. It causes a number of illnesses and joint problems. Complex disorder influenced by a wide range of factors, such as environmental conditions, food, physical activity, lifestyle choices, and heredity. Traditional approaches to evaluating obesity tend to focus on BMI (body mass index) and other basic measures, but these frequently ignore the complexity of the condition and are unable to predict obesity-related hazards. By combining several machine learning and statistical algorithms, a multialgorithm-based obesity analysis system aims to close this gap and provide a analysis of obesity. The system can offer individualized insights into obesity risk and suggest focused therapies by using a variety of data sources, such as food patterns, physical activity levels, genetic predispositions, and medical records. Machine learning (ML) approaches have become effective instruments in medical research to address this problem. Large and complicated datasets can be analyzed using ML algorithms, which can also find subtle patterns and produce predictions that frequently beat those of conventional statistical techniques. ML is being utilized more and more in diabetes research for predictive and diagnostic purposes.
Copyright
Copyright © 2025 MANJUNATH R. This is an open access article distributed under the Creative Commons Attribution License.
