Analysis of Obese Patients Using Machine Learning to Categorize Hidden Risk Factors in Explorative Assessment
R. Krishnamoorthy, Kazuaki Tanaka, R. Thiagarajan
Abstract
Obesity has a multitude of detrimental impacts on both mental and physical health, along with an increased risk of acquiring chronic illnesses including coronary heart disease, diabetes, and stroke, as well as anxiety and depression conditions.Using forecasting analytics, researchers may develop therapies and prevention methods that are tailored to a particular behavior and medical knowledge.Data analytics examines the greater risk of contracting diseases associated with obesity in order to target a specific group.In order to undertake efficient treatments, it is primarily important to gather and evaluate data on issues associated with obesity.Gathering the abnormal diet, lifestyle, and previous medical history tends to indicate whether the individual is affected by an obese hidden risk factor.Machine learning can assist in early diagnosis and anticipate potential health problems associated with obesity.The proposed approach involves various machine learning techniques to analyze comprehensive data on diet, lifestyle.