Enhancement of Infant Health Assessment: Predicting Body Mass Index (BMI) from Real-Time Facial Images Using Machine Learning Techniques

Body Mass Index in infants is a valuable indicator for assessing their growth and nutritional state, helping to identify any potential issues in the early stages. It establishes whether infants are underweight, within a healthy weight range, or overweight, which can specify their overall health. The capability to accurately determine an infant’s BMI can provide significant insight into their general health and helps parents to adopt smart feeding and healthcare decisions. In today’s world, where parents often have limited time and wealth, supporting the proper growth of their infant is vital. The fundamental objective of this research is to propose a user-friendly mobile application which is constructed for predicting infant BMI. This proposed application provides a simple and easy way for parents to track and understand their infant’s nutritionary condition. The proposed application requires only a facial image of an infant, eliminating the necessity for addition parameters such as height, weight. For the prediction of infant BMI, regression algorithms, which includes Linear, Polynomial, Random Forest, and Kernal Ridge, have been employed. The performance of these models has been assessed using several evaluation metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2) score, and the results clearly show their effectiveness in this significant task.