ArticleEndocrine2026
Social determinants of health to predict health-related quality of life in diabetes patients: explainable machine learning approaches.
Article in Endocrine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Epigenetic Regulation Involving microRNAs in Diabetes.Biomolecules · 2026Review
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5 authors.
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Abstract
BACKGROUND &
objectivesSocial determinants of health (SDoH) play a crucial role in shaping health outcomes and quality of life, particularly among individuals living with chronic conditions such as diabetes. Our aim in this study was to develop and validate ensemble ML models that can predict HRQoL in patients with diabetes using social determinants of health (SDoH).
methodsThis cross-sectional study leveraged data from the 2017 Behavioral Risk Factor Surveillance System (BRFSS), targeting adult participants aged 18 years and older with diabetes. Twenty-three predictors, including demographic, socioeconomic, physical, behavioral, mental, and SDoH factors were assessed. The ML model’s performance was examined by calculating precision, recall, F1-score, false positive rate, and the area under the receiver operating characteristic curve (AUROC). Data analysis was performed from August 2024 to September 2024.
resultsAmong 4946 participants, 2381 (48.14%) were aged 65 years or older, and 2815 participants (56.91%) were female. The study population consisted of 142 Asians (2.87%), 538 Blacks (10.88%), 237 Hispanics (4.79%), and 4029 White individuals (81.46%). The AdaBoost model achieved the highest AUROC at 0.70 (95% CI: 0.66–0.73), significantly surpassing the other models. The performance metrics of the AdaBoost model were as follows: accuracy, precision, recall, F1 score, and false positive rate were 0.65 (95% CI: 0.62–0.68), 0.68 (95% CI: 0.64–0.72), 0.73 (95% CI: 0.69–0.76), 0.70 (95% CI: 0.67–0.73), and 0.27 (95% CI: 0.23–0.30), respectively.
conclusionIn this cross-sectional study, we developed and internally validated an ML-based model to identify patients with diabetes at risk for lower HRQoL. The AdaBoost model achieved moderate discriminative performance but showed promise as a screening and prioritization tool to support more personalized and timely interventions. These findings suggest that ML-based risk stratification may complement existing clinical approaches by enabling earlier identification of at-risk individuals; however, external validation and calibration optimization are needed before routine clinical implementation.
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