Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction

• Published in: Scientific reports Vol. 14; no. 1; p. 9143
• Main Authors: Zhang, Lin, Wang, Jixin, Chang, Rui, Wang, Weigang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.04.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 692/699/1541; Accuracy; Algorithms; Autoencoder; Classification; Deep learning; Denoising autoencoder; Disease; Disease detection; Hepatitis; Hepatitis C; Humanities and Social Sciences; multidisciplinary; Neural networks; Predictions; Public health; Science; Science (multidisciplinary); Warning systems; Hepatitis C; Autoencoder; Denoising autoencoder

Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve...