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Related Experiment Video

Updated: Jan 9, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals.

Sanyam Paresh Shah, Abdullah Mamun, Shovito Barua Soumma

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary

    Machine learning models improved metabolic syndrome (MetS) prediction using advanced data balancing and a novel MetaBoost framework. Blood glucose and triglycerides emerged as key predictors for MetS risk reduction.

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    Area of Science:

    • * Computational biology and bioinformatics
    • * Health informatics and machine learning applications

    Background:

    • * Metabolic Syndrome (MetS) is a prevalent condition increasing risks for cardiovascular diseases and type 2 diabetes.
    • * Accurate MetS prediction faces challenges including class imbalance, data scarcity, and methodological inconsistencies.
    • * Existing studies often lack robust optimization for predictive modeling.

    Purpose of the Study:

    • * To systematically evaluate and optimize machine learning (ML) models for enhanced MetS prediction.
    • * To address data imbalance issues using advanced balancing techniques and a novel hybrid framework.
    • * To provide actionable insights into MetS risk factors through counterfactual and probabilistic analyses.

    Main Methods:

    • * Training and comparison of multiple ML models (XGBoost, Random Forest, TabNet) with various data balancing techniques (ROS, SMOTE, ADASYN, CTGAN).
    • * Development and application of MetaBoost, a hybrid framework integrating SMOTE, ADASYN, and CTGAN with weighted averaging and iterative tuning.
    • * Implementation of counterfactual analysis to identify feature modifications for risk reduction and probabilistic analysis for predictor identification.

    Main Results:

    • * The MetaBoost framework achieved up to a 1.87% accuracy improvement over individual balancing techniques.
    • * Counterfactual analysis revealed blood glucose (50.3%) and triglycerides (46.7%) as the most frequently modified features for MetS risk reduction.
    • * Probabilistic analysis identified elevated blood glucose (85.5% likelihood) and triglycerides (74.9% posterior probability) as the strongest predictors of MetS.

    Conclusions:

    • * Advanced ML techniques, particularly the MetaBoost framework, significantly enhance MetS prediction accuracy.
    • * Blood glucose and triglycerides are critical modifiable factors for MetS risk reduction.
    • * This research offers improved methodologies and clinical insights for managing the public health impact of MetS.