Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma

Background Patients with alpha-fetoprotein (AFP)-positive hepatocellular carcinoma (HCC) have aggressive biological behavior and poor prognosis. Therefore, survival time is one of the greatest concerns for patients with AFP-positive HCC. This study aimed to demonstrate the utilization of six machine...

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Veröffentlicht in:	Journal of translational medicine Jg. 22; H. 1; S. 455 - 10
Hauptverfasser:	Dong, Bingtian, Zhang, Hua, Duan, Yayang, Yao, Senbang, Chen, Yongjian, Zhang, Chaoxue
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 13.05.2024 BioMed Central Ltd BMC
Schlagworte:	Algorithms alpha-Fetoproteins - metabolism Area Under Curve Biomedical and Life Sciences Biomedicine Calibration Carcinoma, Hepatocellular - blood Carcinoma, Hepatocellular - diagnosis Carcinoma, Hepatocellular - mortality Carcinoma, Hepatocellular - pathology Computational Modelling and Epidemiology Epidemiology Female Glycoproteins Hepatocellular carcinoma Hepatoma Humans Liver Neoplasms - blood Liver Neoplasms - diagnosis Liver Neoplasms - mortality Liver Neoplasms - pathology Machine Learning Male Medical research Medicine, Experimental Medicine/Public Health Predictive analytics Prognosis ROC Curve Survival XGBoost algorithm Taiwan Hepatocellular carcinoma XGBoost algorithm Predictive analytics Survival Machine learning
ISSN:	1479-5876, 1479-5876
Online-Zugang:	Volltext
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