PREDICTING OBESITY TRENDS: THE ROLE OF DIETARY HABITS THROUGH MACHINE LEARNING MODELS

Obesity is a growing global health concern, influenced by a complex interplay of genetic, environmental, and lifestyle factors.Among these, dietary habits are a key contributor, making it essential to understand how eating behaviors impact obesity levels.This research investigates the use of machine learning (ML) techniques to predict obesity based on individuals' dietary patterns and demographic characteristics.A comprehensive dataset containing information on age, gender, eating frequency, food type, physical activity, and obesity levels is utilized.Several machine learning algorithms, including Decision Trees, Support Vector Machines (SVM), Random Forests are implemented to develop predictive models.Feature selection techniques are applied to identify the most influential variables affecting obesity.Model performance is evaluated using standard classification metrics such as accuracy, precision, recall, and F1-score.The results demonstrate that ML algorithms can effectively predict obesity levels, with some models outperforming others in terms of accuracy and generalization.Notably, feature importance analysis reveals specific dietary behaviors and food groups-such as high consumption of fast food, sugary beverages, and low physical activity levels-as strong predictors of obesity.This research underscores the potential of machine learning in public health by offering data-driven insights into the dietary factors contributing to obesity.The findings can inform the development of personalized nutrition recommendations, targeted health interventions, and evidence-based public health policies.Ultimately, this work contributes to the advancement of predictive healthcare analytics and supports efforts to combat the global obesity epidemic through informed decision-making and lifestyle modifications.