PIPR Machine Learning Model: Obesity Impact Analysis

Abstract

Introduction Obesity is a prevalent and multifaceted health hazard globally, necessitating effective predictive models to mitigate its impact on chronic diseases. Methods This paper introduces the Protein Food Item Prediction Regression (PIPR) model, employing machine learning techniques to analyze the influence of protein-rich foods on obesity. The model undergoes rigorous preprocessing and iterative refinement to identify correlated variables and predict obesity trends. Results The PIPR model demonstrates superior performance in predicting obesity trends, showcasing lower error rates and high adjusted R2 values. For instance, for the most correlated variables like Meat and Milk (including butter), the model exhibits impressive performance with an MSE of 49.59, RMSE of 7.04, MAE of 5.08, and MAPE of 29%. Similarly, for the least correlated variables like oil crops and vegetable products, the PIPR model maintains excellence with an MSE of 52.51, RMSE of 7.24, MAE of 5.39, and MAPE of 31%. Conclusion The PIPR model emerges as a promising tool for understanding and addressing obesity's complexities, offering valuable insights into dietary patterns and potential interventions. Further research and validation could enhance its applicability and effectiveness in combating obesity on a global scale.