ArticleHeliyon2024
A machine learning-based predictive model discriminates nonalcoholic steatohepatitis from nonalcoholic fatty liver disease.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of machine learning and deep learning in metabolic dysfunction-associated steatotic liver disease: a systematic review and meta-analysis.Journal of advanced research · 2026Pooled it
- First trimester hepatic steatosis index as a predictor of gestational diabetes mellitus: a cohort study in Northwestern China.Scientific reports · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) is a leading cause of liver-related morbidity and mortality. The diagnosis of non-alcoholic steatohepatitis (NASH) plays a crucial role in the management of NAFLD patients. Objective: The aim of our observational study was to build a machine learning model to identify NASH in NAFLD patients. Methods: The clinical characteristics of 259 NAFLD patients and their initial laboratory data (Cohort 1) were collected to train the model and carry out internal validation. We compared the models built by five machine learning algorithms and screened out the best models. Receiver operating characteristic (ROC) curves, sensitivity, specificity, and accuracy were used to evaluate the performance of the model. In addition, the NAFLD patients in Cohort 2 (n = 181) were externally verified. Results: We finally identified six independent risk factors for predicting NASH, including neutrophil percentage (NEU%), aspartate aminotransferase/alanine aminotransferase (AST/ALT), hematocrit (HCT), creatinine (CREA), uric acid (UA), and prealbumin (PA). The NASH-XGB6 model built using the XGBoost algorithm showed sufficient prediction accuracy, with ROC values of 0.95 (95 % CI, 0.91-0.98) and 0.90 (95 % CI, 0.88-0.93) in Cohort 1 and Cohort 2, respectively. Conclusions: NASH-XGB6 can serve as an effective tool for distinguishing NASH patients from NAFLD patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.