Artificial Neural Network Model for Predicting 5-year Mortality after Surgery for Hepatocellular Carcinoma and Performance Comparison with Logistic Regression Model: A Nationwide Taiwan Database Study

2012 ◽  
Author(s):  
Wan-Ting Hung ◽  
King-Teh Lee ◽  
Shih-Chin Wang ◽  
Wen-Hsien Ho ◽  
Su-Ching Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Hepatocellular Carcinoma ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Regression Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Logistic Regression Model ◽  
Performance Comparison ◽  
And Performance

In-Hospital Mortality Prediction in Patients Receiving Mechanical Ventilation in Taiwan

2013 ◽  
Vol 22 (6) ◽  
pp. 506-513 ◽  
Author(s):  
Chao-Ju Chen ◽  
Hon-Yi Shi ◽  
King-Teh Lee ◽  
Tzuu-Yuan Huang
Keyword(s):  
Neural Network ◽  
Mechanical Ventilation ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Logistic Regression Model ◽  
Artificial Neural

Background Few studies have used pooled data for more than 2 years and few have analyzed data for patients receiving mechanical ventilation in Taiwan. Objective To validate the use of an artificial neural network model for predicting in-hospital mortality in patients receiving mechanical ventilation in Taiwan and to compare the predictive accuracy of the artificial neural network model with that of a logistic regression model. Methods Retrospective comparison of 1000 pairs of data sets processed by logistic regression and artificial neural network models based on initial clinical data for 213 945 patients receiving mechanical ventilation. For each pair of artificial neural network and logistic regression models, the area under the receiver operating characteristic curves, Hosmer-Lemeshow statistics, and accuracy rate were calculated and compared by using t tests. Global sensitivity analysis and sensitivity score approach were also used to assess the relative significance of input parameters in the system model and the relative importance of variables. Results Compared with the logistic regression model, the artificial neural network model had a better accuracy rate in 96.3% of cases, better Hosmer-Lemeshow statistics in 41.2% of cases, and a better area under the curve in 97.6% of cases. Hospital volume was the most influential (sensitive) variable affecting in-hospital mortality, followed by Charlson comorbidity index, length of stay, and hospital type. Conclusions Compared with the conventional logistic regression model, the artificial neural network model was more accurate in predicting in-hospital mortality and had higher overall performance indices.

The Artificial Neural Network and Regression Analysis Approach to Prediction of the Fiber Diameter of Spunbonding Fabrics

2010 ◽  
Vol 33 ◽  
pp. 74-78
Author(s):  
B. Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Process Parameters ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Statistical Regression ◽  
Artificial Neural

In this work, the artificial neural network model and statistical regression model are established and utilized for predicting the fiber diameter of spunbonding nonwovens from the process parameters. The artificial neural network model has good approximation capability and fast convergence rate, which is used in this research. The results show the artificial neural network model can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the statistical regression model, which reveals that the artificial neural network model is based on the inherent principles, and it can yield reasonably good prediction results and provide insight into the relationship between process parameters and fiber diameter.

Sign in / Sign up

Export Citation Format

Share Document