ESTIMATIVAS DE NÍVEIS DE OBESIDADE UTILIZANDO MACHINE LEARNING

EXPLORANDO FATORES CONTRIBUTIVOS E MODELOS PREDITIVOS PARA A PREVENÇÃO E INTERVENÇÃO NA OBESIDADE

Abstract

This article explores the use of advanced machine learning techniques to estimate obesity levels based on demographic data, eating habits and physical condition of individuals from Mexico, Peru and Colombia. Using a robust and diverse dataset, the study employs algorithms such as logistic regression, decision trees and neural networks to develop predictive models. Analysis includes everything from data preparation to evaluating the effectiveness of models, providing valuable insights for public health interventions and policies aimed at preventing and treating obesity.