ArticleHeliyon2022
Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed, 20 citations in OpenAlex.
- Development and validation of machine learning models for diagnosing hepatocellular carcinoma risk and survival in patients with diabetic cirrhosis.Scientific reports · 2026Article
- An intelligent healthcare framework for hepatocellular carcinoma diagnosis based on aggregated learners from biomedical data utilising explainable artificial intelligence.Scientific reports · 2026Article
- Appraisal of Clinical Explanatory Variables in Subtyping of Type 2 Diabetes Using Machine Learning Models.Journal of clinical medicine · 2025Article
- Link between type 2 diabetes mellitus and hepatocellular carcinoma.World journal of hepatology · 2025Review
- Risk of Microvascular Complications in Newly Diagnosed Type 2 Diabetes Patients Using Automated Machine Learning Prediction Models.Journal of clinical medicine · 2024Article
- Review
- Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Hepatocellular carcinoma (HCC) among type-2 diabetes (T2D) patients is an increasing burden to diabetes management. This study aims to develop and select the best machine learning (ML) classification model for predicting HCC in T2D for HCC early detection. Methods: A case-control study was conducted utilising computerised medical records in two hepatobiliary centres. The predictors were chosen using multiple logistic regression. IBM SPSS Modeler® was used to assess the discriminative performance of support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), chi-square automatic interaction detection (CHAID), and their ensembles. Results: Subjects (N = 424) were split into 60% training (n = 248) and 40% testing (n = 176) groups. The independent predictors identified were race, viral hepatitis, abdominal pain/discomfort, unintentional weight loss, statins, alcohol consumption, non-alcoholic fatty liver, platelet <150 ×10 Conclusions: If further validation studies confirm these results, the SVM model's application potentially augments early HCC detection in T2D 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 OpenQuestion graph.