10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Hasti Vakani, Mithil Mistry, Hardikkumar Jayswal, Nilesh Dubey, Nitika Sharma,Rohan Patel, Dipika Damodar Abstract - Obesity has become a significant global health concern due to its as-sociation with various non-communicable diseases. Traditional methods for obesity assessment, such as BMI, often fail to capture the complexity of the condition, highlighting the need for more accurate predictive tools. This research utilize the machine learning algorithms, including Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks, in a stacking ensemble model to predict obesity levels. Utilizing datasets from diverse populations, the model achieved a high accuracy of 96.69%. Key features such as BMI, age, and dietary habits were identified as critical predictors through Recursive Feature Elimination. The research findings demonstrate the potential of advanced data-driven techniques in providing personalized insights into obesity management and underscore the transformative role of machine learning in public health initiatives.
