Obesity Detection in Adults using Machine Learning

A critical worldwide health issue, obesity necessitates prompt and accurate identification for effective intervention. This study tackles the urgent problem of overweight and obesity, which present serious public health issues because of their links to a number of illnesses, elevated rates of morbidity, and elevated mortality risks. Because it provides the possibility for precise risk identification and intervention techniques to reduce these risks and enhance public health outcomes, this research is essential for addressing the growing health concerns related with obesity and overweight[4]. Empirical findings indicate that, based on the characteristics provided, machine learning [2] algorithms can accurately detect obesity. The algorithms' efficacy is evaluated using metrics like F1 score, precision, and recall. The study also explores the interpretability of the machine learning model and important characteristics impacting the predictions of obesity[5]. In summary, this study contributes to the nexus between artificial intelligence and healthcare by highlighting the viability and effectiveness of using ML algorithms for obesity identification. The research results establish the foundation for creating useful instruments to tackle the worldwide obesity crisis. In the future, the model might be improved, more data sources added, and application tactics for broader impact investigated.