Interactive Real-Time Weight Management Platform Using Machine Learning Methods - Haksa, Febrina Rosadah; Endri, Jon; Aryanti, Aryanti (Universitas Islam Negeri Sultan Syarif Kasim Riau, 2025)
This research develops an interactive and real-time web-based weight management platform that integrates machine learning methods using decision tree algorithms to detect the risk of weight-related diseases. The platform features an automatic Body Mass Index (BMI) calculator as well as a risk prediction system for diseases such as obesity and cardiovascular disorders. The data used includes the user's weight, height, eating habits, and physical activity level parameters collected through a live user interface. Based on the data, a decision tree algorithm is used to classify the health risk level and provide personalized recommendations to the user to help with preventive weight management. Initial testing showed that the decision tree model applied was able to achieve a prediction accuracy rate of 97%, demonstrating reliable performance in identifying health risks based on lifestyle data. This platform is expected to be an accessible technology solution to increase public awareness of the importance of weight management and disease prevention independently.