A machine learning approach for the early detection of obesity and overweight achieved an accuracy of approximately 90%.
Observational
Can a machine learning model accurately detect obesity and overweight based on BMI?
A machine learning model using BMI data from an educational setting can detect obesity with approximately 90% accuracy.
More than 2.1 billion people worldwide are shuddering from overweightness or obesity, which represents approximately 30% of the world’s population. Obesity is a serious global health problem. By 2030, 41% of people will likely be overweight or obese, if the current trend continues. People who show indications of weight increase or obesity run the danger of contracting life-threatening conditions including type 2 diabetes, respiratory issues, heart disease, and stroke. Some intervention strategies, like regular exercise and a balanced diet, might be essential to preserving a healthy lifestyle. Thus, it is crucial to identify obesity as soon as feasible. We have collected data from sources like schools and colleges within our organization to create our dataset. A vast range of ages is considered and the BMI value is examined in order to determine the level of obesity. The dataset of people with normal BMI and those at risk has an inherent imbalance. The outcomes are collected and showcased via a website which also includes various preventive measures and calculators. The outcomes are promising, and clock an accuracy of about 90%.
Sable et al. (Fri,) conducted a observational in Obesity and Overweight. Machine learning approach was evaluated on Model accuracy for detecting obesity. A machine learning approach for the early detection of obesity and overweight achieved an accuracy of approximately 90%.