What question did this study set out to answer?

This research aims to classify metabolic syndrome control among patients with non-communicable diseases using machine learning techniques.

June 27, 2026Scientific Reports0 citationsOpen Access

Bayesian-optimized machine learning models for classifying metabolic syndrome control among NCD patients in Bangladesh hospitals

MHMd Soumike HassanJahangirnagar University JPJakia Sultana PingkyJahangirnagar University ADA. DharJahangirnagar University

Key Points

This research aims to classify metabolic syndrome control among patients with non-communicable diseases using machine learning techniques.
Cross-sectional study conducted across 14 hospitals in Bangladesh with 472 patients aged 40 and above.
Developed and evaluated six classifiers using Bayesian optimization and various statistical methods for analysis.
Utilized feature selection and interpretability techniques like SHAP to identify important factors related to metabolic syndrome control.
51.1% of participants achieved control of metabolic syndrome.
Random Forest classifier showed best performance with CV AUC of 0.789, accuracy of 72.9%, and F1-score of 0.735.
Key factors associated with better metabolic syndrome control included physical activity, adherence to medication, and follow-up care.

Abstract

Control of metabolic syndrome (MS) is still a major challenge for patients suffering from Non-communicable diseases (NCDs) in Bangladesh. In this study, the status of MS control was determined, and Bayesian-optimized machine-learning models were developed and tested to classify MS control status concurrently with NCDs in patients attending government hospitals. The study was a cross-sectional hospital-based study in 14 government hospitals of Bangladesh from June to July 2025. A total of 472 adult NCD patients aged 40 years and above were included. The clinical, behavioral, demographic, and health-system-related variables were examined. Boruta feature selection was used, and six classifiers (Random Forest, Multilayer Perceptron, CatBoost, XGBoost, LightGBM, and AdaBoost) were evaluated by means of 10-fold stratified cross-validation, bootstrap 95% confidence intervals, calibration analysis, seed-variance analysis, McNemar’s test, and interpretability based on SHAP values. Overall, 51.1% of participants achieved control of MS. Better control was related to secondary level hospitals, physical activity, medicine adherence, and community participation, follow-up care, and health behavior monitoring, while poorer control was related to polypharmacy, higher burden of comorbidities, and abnormal BMI. Among six Bayesian-optimized classifiers, Random Forest demonstrated the best overall performance (CV AUC: 0.789 ± 0.062; accuracy: 72.9%; F1-score: 0.735; bootstrap 95% CI: 0.630–0.832) and exhibited the fewest false negatives with optimal calibration. Comorbidity, polypharmacy, physical activity, and BMI were the most important factors associated with MS control as identified by SHAP analysis. Bayesian-optimized Random Forest is a viable and meaningful solution for an interpretable, concurrent classification of the control status of MS in the context of limited resources within a hospital environment. The results back the integrated management of comorbidities, medication review, promotion of physical activity, weight management, and structured NCD follow-up.

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Cite This Study

Hassan et al. (Thu,) studied this question.

synapsesocial.com/papers/6a3f6817aea7db3c1953f403 https://doi.org/https://doi.org/10.1038/s41598-026-59451-5

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