1. Artificial Neural Network Model Is Superior to Logistic Regression Model in Predicting Treatment Outcomes of Interferon-Based Combination Therapy in Patients with Chronic Hepatitis C.
- Author
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Chun-Hsiang Wang, Lein-Ray Mo, Ruey-Chang Lin, Jen-Juan Kuo, Kuo-Kuan Chang, and Jieh-Jen Wu
- Subjects
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HEPATITIS C , *VIRAL hepatitis , *INTERFERONS , *ANTINEOPLASTIC agents , *GLYCOPROTEINS , *ANTIVIRAL agents , *ARTIFICIAL neural networks - Abstract
Background/Aims: Patients with chronic hepatitis C (CHC) can achieve a sustained virologic response if they received pegylated interferon plus ribavirin therapy; however, some of them do not respond or relapse after treatment. The aim of this study was to compare the ability of two statistical models to predict treatment outcomes. Methods: Clinical data, biochemical values, and liver histological features of 107 patients with CHC were collected and assessed using a logistic regression (LR) model and an artificial neural network (ANN) model. Both the LR and ANN models were compared by receiver-operating characteristics curves. Results: Aspartate aminotransferase (p = 0.017), prothrombin time (p = 0.002), body mass index (BMI; p = 0.003), and fibrosis score of liver histology (p = 0.002) were found to be significant predictive factors by univariate analysis. The independent significant predicting factor was BMI by multivariate LR analysis (p = 0.0095). The area under receiver-operating characteristics of the ANN model was larger than that of the LR model (85 vs. 58.4%). Conclusions: It was found that BMI is an independent factor for identifying patients with favorable treatment response. A useful ANN model in predicting outcomes of standard treatment for CHC infection was developed and showed greater accuracy than the LR model. Copyright © 2008 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]
- Published
- 2008
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