Predictive Modeling of Weight Loss and Metabolic Health Outcomes: A Retrospective Predictive Modeling Study

Area of Science:

• Obesity research
• Machine learning in healthcare
• Metabolic syndrome studies

Background:

• Obesity affects 13% of adults globally, increasing mortality and reducing quality of life.
• Hypocaloric diets with a Mediterranean pattern are used to manage obesity and metabolic issues.

Purpose of the Study:

• To develop and evaluate machine learning (ML) models for predicting body weight loss and metabolic syndrome (MetS) changes.
• To assess model performance in obese patients undergoing a 3-month hypocaloric Mediterranean diet intervention.

Main Methods:

• Utilized a dataset of 893 obese patients from a clinical trial.
• Implemented five ML models: Logistic Regression, Decision Tree, Random Forest, XGBoost, and Support Vector Classifier.
• Assessed model performance using accuracy, precision, recall, F1-score, and ROC curves.

Main Results:

• Stacking and Random Forest models showed high accuracy for predicting body weight loss.
• Stacking achieved the best performance for predicting MetS change (85.90% accuracy, 83.65% AUC).
• The combined prediction model for weight loss and MetS change was most accurate with Stacking (94.74% accuracy, 95.35% AUC).

Conclusions:

• Ensemble ML methods, particularly Stacking and XGBoost, effectively predict outcomes in obese patients on a Mediterranean diet.
• Predictive accuracy is influenced by metabolic and inflammatory markers, insulin resistance, and age.
• Integrating these ML tools can personalize dietary interventions for improved obesity management.