Integrating epidemiologic modeling and explainable machine learning to predict and identify factors associated with self-reported depression among adults in Tennessee, United States
Mustapha Aliyu Muhammad, Jamilu Sani, Salad Halane · 2026
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Description
Mustapha Aliyu Muhammad and others modeled low mood in adults in Tennessee in 2026, joining stats with clear machine learning. Tennessee ranks high for low mood in the US, yet many cases go unseen for want of screening and care. The team used 2023 survey data for the state.
A cross-section of 5,596 adults, standing for over 5.5 million folk, gave a lifetime low-mood share of 27.3 percent. Eight learning models were trained with split checks and then tested on Oregon data, scored by hit rate, true rate, and like measures. SHAP plots showed which traits drove each guess. Health teams gain both a state baseline and a clear model to aim screening at.
Catalog record
| CREATOR | Mustapha Aliyu Muhammad, Jamilu Sani, Salad Halane |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |