Comprehensive and low-cost machine learning models for predicting MASLD prevalence: cross-sectional evidence from the amol cohort study (AmolCS).
This cross-sectional study from the Amol Cohort developed and validated low-cost machine learning models for predicting metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence using widely accessible clinical and anthropometric indicators in an Iranian population. Visceral adiposity and metabolic dysfunction emerged as key predictors, and the models' reliance on accessible inputs suggests potential feasibility for scalable MASLD screening in resource-limited settings.
This cross-sectional study from the Amol Cohort developed and validated low-cost machine learning models for predicting metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence using widely accessible clinical and anthropometric indicators in an Iranian population. Visceral adiposity and metabolic dysfunction emerged as key predictors, and the models' reliance on accessible inputs suggests potential feasibility for scalable MASLD screening in resource-limited settings.
What the study was
- Study design
- Cohort study
- Sample size
- 2184
- Category
- Other
- Maturity
- Validated
- Journal
- Journal of health, population, and nutrition
Why it surfaced
Relevant Sentinel scan study (promising preliminary signal) meets standard-priority threshold.
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