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This article discusses the use of machine learning techniques to predict non-alcoholic steatohepatitis (NASH) based on clinical and blood data. The study found that random forest, combined with feature selection, had the best performance in accurately diagnosing NASH. This has the potential to improve early and non-invasive diagnosis of NASH, a widespread and serious health concern.
