ArticleDiagnostics (Basel, Switzerland)2023
A Machine Learning-Based Method for Detecting Liver Fibrosis.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploratory Comparison of Meld, Meld-Na, and Meld 3.0 Scores for Prognostic Assessment at Diagnosis in Hepatocellular Carcinoma Using Machine Learning Approaches.Digestive diseases and sciences · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Artificial Intelligence for Fibrosis Diagnosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification.Life (Basel, Switzerland) · 2025Article
- Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Article
- Application of Machine Learning Techniques to Assess Alpha-Fetoprotein at Diagnosis of Hepatocellular Carcinoma.International journal of molecular sciences · 2024Article
- Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves.Viruses · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.
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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 OpenQuestion graph.