Key result
The Random Forest model outperformed other machine learning algorithms in classifying complex multimorbidity clusters, achieving an AUROC of 0.805.
Why the study?
Complex multimorbidity, particularly the intersection of cardiometabolic disorders and mental health conditions, poses a serious threat to public health systems and requires priority interventions.
Population
46,736 responses from the CDC BRFSS 2015 dataset
Comparison
Six machine learning algorithms (MLR, MNB, DT, RF, XGB, and ANN)
Design
Latent class analysis and machine learning classification study
Authors
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May inform targeted multimorbidity interventions; hypothesis-generating from cross-sectional data and requires prospective validation.
Cross-Sectional (n=46,736)
Effect estimate: AUROC 0.805 (95% CI 0.800-0.809)
p-value: p=0.0000
Machine learning models, particularly Random Forest, can accurately classify complex cardiometabolic and mental health multimorbidity clusters, providing a decision support tool for targeted public health interventions.
Mukherjee et al. (2025) conducted a cross-sectional in Complex multimorbidity (cardiometabolic and mental health conditions) (n=46,736). Random Forest machine learning model vs. Other machine learning models (MLR, MNB, DT, XGB, ANN) was evaluated on Classification of complex multimorbidity clusters (AUROC) (AUROC 0.805, 95% CI 0.800-0.809, p=0.0000). The Random Forest model outperformed other machine learning algorithms in classifying complex multimorbidity clusters, achieving an AUROC of 0.805.
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