title: 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.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Hepatogastroenterology, Tainan Municipal Hospital, Tainan, Taiwan, ROC. chunhsiang@gmail.com
authors.forename: Chun-Hsiang
authors.initials: CH
authors.lastname: Wang
authors.forename: Lein-Ray
authors.initials: LR
authors.lastname: Mo
authors.forename: Ruey-Chang
authors.initials: RC
authors.lastname: Lin
authors.forename: Jen-Juan
authors.initials: JJ
authors.lastname: Kuo
authors.forename: Kuo-Kuan
authors.initials: KK
authors.lastname: Chang
authors.forename: Jieh-Jen
authors.initials: JJ
authors.lastname: Wu
mesh_terms: D000328:Adult; D000368:Aged; D001219:Aspartate Aminotransferases / Q000097:blood; D015992:Body Mass Index; D004341:Drug Evaluation / Q000706:statistics & numerical data*; D005260:Female; D019698:Hepatitis C, Chronic / Q000188:drug therapy* / Q000473:pathology / Q000503:physiopathology / Q000821:virology; D006801:Humans; D007372:Interferons / Q000627:therapeutic use*; D008099:Liver / Q000201:enzymology / Q000473:pathology; D008103:Liver Cirrhosis / Q000473:pathology; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011517:Prothrombin Time; D012720:Severity of Illness Index; D016896:Treatment Outcome; D019562:Viral Load
chemical_list: D007372:Interferons; D001219:Aspartate Aminotransferases