Authors : Noor Atika Azit, Shahnorbanun Sahran, Voon Meng Leow, Manisekar Subramaniam, Suryati Mokhtar, Azmawati Mohammed Nawi
Title of Publication : Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model
Journal Name : Heliyon
Quartile : Q2
Impact Factor : 3.776
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.
Conclusions: If further validation studies confirm these results, the SVM model's application potentially augments early HCC detection in T2D patients.