NDLI: Prediction of survival in patients with liver cancer using artificial neural networks and classification and regression trees

Content Provider	IEEE Xplore Digital Library
Author	Cheng-Mei Chen Chien-Yeh Hsu Hung-Wen Chiu Hsiao-Hsien Rau
Copyright Year	2011
Description	Author affiliation: Graduate Institute of Medical Informatics, Taipei Medical University, TAIWAN, R.O.C (Cheng-Mei Chen; Chien-Yeh Hsu; Hung-Wen Chiu; Hsiao-Hsien Rau)
Abstract	This study established a survival prediction model for liver cancer using data mining technology. The data were collected from the cancer registration database of a medical center in Northern Taiwan between 2004 and 2008. A total of 227 patients were newly diagnosed with liver cancer during this time. With literature review, and expert consultation, nine variables pertaining to liver cancer survival were analyzed using t-test and chi-square test. Six variables showed significant. Artificial neural network (ANN) and classification and regression tree (CART) were adopted as prediction models. The models were tested in three conditions; one variable (clinical stage alone), six significant variables, and all nine variables (significant and non significant). 5-year survival was the output prediction. The results showed that the ANN model with nine input variables was superior predictor of survival (p<0.001). The area under receiver operating characteristic curve (AUC) was 0.915, 0.87, 0.88, and 0.87 for accuracy, sensitivity, and specificity respectively. The ANN model is significant more accurate than CART model when predict survival for liver cancer and provide patients information for understanding the treatment outcomes.
Starting Page	811
Ending Page	815
File Size	300754
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022187
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training artificial neural networks Accuracy prediction model Input variables Liver Artificial neural networks Predictive models liver cancer classification and regression trees Cancer
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting patients survival using supervised techniques

Using neural networks committee machines to improve outcome prediction assessment in nonlinear regression

Rainfall Prediction Using Generalized Regression Neural Network: Case Study Zhengzhou

A Comparison of Artificial Neural Network and Decision Trees with Logistic Regression as Classification Models for Breast Cancer Survival

Prediction of wastewater treatment plant performance using multilinear regression and artificial neural networks

Prediction of Indoor Air Quality Using Artificial Neural Networks

Comparison of artificial neural network and logistic regression models for predicting in-hospital survival after hepatocellular carcinoma surgery

Predictive models for prostate cancer based on logistic regression and artificial neural network

Multi-zone temperature prediction in a commercial building using artificial neural network model

Prediction of survival in patients with liver cancer using artificial neural networks and classification and regression trees

Similar Documents

Predicting patients survival using supervised techniques

Using neural networks committee machines to improve outcome prediction assessment in nonlinear regression

Rainfall Prediction Using Generalized Regression Neural Network: Case Study Zhengzhou

A Comparison of Artificial Neural Network and Decision Trees with Logistic Regression as Classification Models for Breast Cancer Survival

Prediction of wastewater treatment plant performance using multilinear regression and artificial neural networks

Prediction of Indoor Air Quality Using Artificial Neural Networks

Comparison of artificial neural network and logistic regression models for predicting in-hospital survival after hepatocellular carcinoma surgery

Predictive models for prostate cancer based on logistic regression and artificial neural network

Multi-zone temperature prediction in a commercial building using artificial neural network model

Prediction of survival in patients with liver cancer using artificial neural networks and classification and regression trees