Analyzing the physical activity and exercise management for obesity and weight loss based on fuzzy logic

Over the past three decades, an increase in overweight and obesity has devastated public health. A better diet and increased exercise are encouraged by approaches for reducing obesity (PA). Two significant public health problems contributing to the rise in chronic illnesses that cannot be prevented are obesity and physical inactivity. Obese people cannot perform the necessary level of physical activity due to their lack of physical fitness and comorbidities. The obesity paradox, early weight loss, maintenance, and the importance of physical activity and exercise training (PA-ET) for avoiding body weight increase are all covered in this study. This research blends machine learning algorithms and a data-driven methodology to identify patterns in profiles related to weight change in a nutrition intervention programme. This study builds a web-based weight loss reminder system using fuzzy logic that allows users to keep track of their weight, caloric consumption, and physical activity. Aerobic ET programmes advocated for public health can result in moderate weight loss; however, individual weight loss varies greatly. Doctors should emphasize that even if patients don't lose weight, they can still benefit from their physical activity programme and educate patients about reasonable expectations. PA programmes continue to have a lot of health advantages. Finally, evidence demonstrates that obesity is treatable and preventable. It is possible to maintain weight loss attained with diet and exercise. This framework's main goal is to discover different thresholds that are susceptible to the harmful effects of obesity on health, and it was able to do so with 93.4 per cent efficiency.