Monitoring and Predicting Overweight and Obesity Using Machine Learning

Obesity is a significant public health issue worldwide, with an increasing number of people being affected by it. The problem has become a leading cause of several life-threatening health conditions such as diabetes, cardiovascular disease, and cancer. Early detection and intervention are crucial for obesity prevention and management. Machine learning (ML) techniques can help to address this issue by providing advanced tools for monitoring and predicting obesity. This paper presents a review of recent research on monitoring and predicting overweight and obesity using ML. It discusses the importance of monitoring and predicting obesity and how ML techniques can be applied to this area. The paper also highlights various factors that contribute to obesity, such as lifestyle, genetics, and environmental factors. The review identifies several ML algorithms that have been used for monitoring and predicting obesity, including decision trees, support vector machines, and neural networks. It also examines the role of various data sources such as demographics, health history, and lifestyle factors in building predictive models. Finally, the paper discusses the potential benefits of using ML for obesity monitoring and prevention, including the ability to identify high-risk patients early and to intervene with personalized and targeted care. The paper concludes that ML can significantly improve obesity monitoring and treatment by providing healthcare providers with more accurate and timely information on patients' health status.