An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model...

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Bibliographic Details
Published in:	Medical & biological engineering & computing Vol. 56; no. 10; pp. 1771 - 1779
Main Authors:	Zhang, Li, Chen, Jiasheng, Gao, Chunming, Liu, Chuanmiao, Xu, Kuihua
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2018 Springer Nature B.V
Subjects:	Adult Aged Aged, 80 and over Algorithms Antigens Artificial Intelligence Artificial neural networks Biomarkers Biomarkers, Tumor - blood Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Carbohydrates Carcinoembryonic antigen Carcinoma, Hepatocellular - blood Carcinoma, Hepatocellular - diagnosis Carcinoma, Hepatocellular - genetics Case-Control Studies Computer Applications Diagnosis Evolutionary algorithms Female Gene expression Gene Expression Regulation, Neoplastic Hepatocellular carcinoma Human Physiology Humans Imaging Liver cancer Liver Neoplasms - blood Liver Neoplasms - diagnosis Liver Neoplasms - genetics Male Mathematical models Medical diagnosis Middle Aged Multilayer perceptrons Neural networks Original Article Problem solving Radiology ROC Curve Test sets Young Adult α-Fetoprotein γ-Glutamyltransferase Hepatocellular carcinoma Auxiliary diagnosis Serum biomarkers Gene expression programming
ISSN:	0140-0118, 1741-0444, 1741-0444
Online Access:	Get full text
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