Trial reportComputational and mathematical methods in medicine2022
Risk Factors and Prediction Models for Nonalcoholic Fatty Liver Disease Based on Random Forest.
Trial report in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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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
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 11 citations in OpenAlex.
- Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- Trends in AI-based diagnosis and intervention of metabolic diseases: a bibliometric analysis of the literature from 2000 to 2024.Frontiers in medicine · 2025Pooled it
- Association between GNRI and risk of non-alcoholic fatty liver disease in non-obese individuals: a Chinese longitudinal prospective cohort study.Frontiers in nutrition · 2025Article
- Association between paravertebral muscle radiological parameter alterations and non-alcoholic fatty liver disease.Abdominal radiology (New York) · 2024Article
- Androgens exacerbate hepatic triglyceride accumulation in rats with polycystic ovary syndrome by downregulating MTTP expression.Endocrine · 2024Article
- Lipoprotein Combine Index as a Better Marker for NAFLD Identification Than Traditional Lipid Parameters.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Causal relationship between air pollution, lung function, gastroesophageal reflux disease, and non-alcoholic fatty liver disease: univariate and multivariate Mendelian randomization study.Frontiers in public health · 2024Article
- Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy.World journal of gastroenterology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To establish a risk prediction model of nonalcoholic fatty liver disease (NAFLD) and provide management strategies for preventing this disease. Methods: A total of 200 inpatients and physical examinees were collected from the Department of Gastroenterology and Endocrinology and Physical Examination Center. The data of physical examination, laboratory examination, and abdominal ultrasound examination were collected. All subjects were randomly divided into a training set (70%) and a verification set (30%). A random forest (RF) prediction model is constructed to predict the occurrence risk of NAFLD. The receiver operating characteristic (ROC) curve is used to verify the prediction effect of the prediction models. Results: The number of NAFLD patients was 44 out of 200 enrolled patients, and the cumulative incidence rate was 22%. The prediction models showed that BMI, TG, HDL-C, LDL-C, ALT, SUA, and MTTP mutations were independent influencing factors of NAFLD, all of which has statistical significance ( Conclusion: This study established a prediction model of NAFLD occurrence risk based on the RF, which has a good prediction value.
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