Minimal clinical predictors enable machine learning detection of hepatocellular carcinoma in a Filipino cohort.

ABSTRACT: Despite significant advancements in the diagnosis and treatment of Hepatocellular carcinoma (HCC), the overall prognosis remains challenging due to its multifactorial nature. Recently, machine learning has been increasingly utilized in HCC-related research, offering significant advancements in diagnostic accuracy and early detection. However, the application of these techniques in diverse populations, particularly underrepresented groups, remains limited. In this study, seven machine learning algorithms: logistic regression (LR), k-nearest neighbor (KNN), support vector classifier (SVC), decision tree (DT), random forest (RF), stochastic gradient boosting (GB) and LightGBM, were tested. Optimal hyperparameters were determined using a grid-search approach, and several feature selection tech

SUBMITTER: Roxas-Villanueva RML 

PROVIDER: S-EPMC12606090 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature