Improving gastric cancer outcome prediction using single time-point artificial neural network models
In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time...
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| Format: | Article |
| Language: | English |
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Sage Publications
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/62125/ http://psasir.upm.edu.my/id/eprint/62125/1/Improving%20gastric%20cancer.pdf |
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| author | Dezfouli, Hamid Nilsaz Abu Bakar, Mohd Rizam Arasan, Jayanthi Adam, Mohd Bakri Pourhoseingholi, Mohamad Amin |
| author_facet | Dezfouli, Hamid Nilsaz Abu Bakar, Mohd Rizam Arasan, Jayanthi Adam, Mohd Bakri Pourhoseingholi, Mohamad Amin |
| author_sort | Dezfouli, Hamid Nilsaz |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. |
| first_indexed | 2025-11-15T11:11:52Z |
| format | Article |
| id | upm-62125 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:11:52Z |
| publishDate | 2017 |
| publisher | Sage Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-621252019-04-15T04:22:55Z http://psasir.upm.edu.my/id/eprint/62125/ Improving gastric cancer outcome prediction using single time-point artificial neural network models Dezfouli, Hamid Nilsaz Abu Bakar, Mohd Rizam Arasan, Jayanthi Adam, Mohd Bakri Pourhoseingholi, Mohamad Amin In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. Sage Publications 2017 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/62125/1/Improving%20gastric%20cancer.pdf Dezfouli, Hamid Nilsaz and Abu Bakar, Mohd Rizam and Arasan, Jayanthi and Adam, Mohd Bakri and Pourhoseingholi, Mohamad Amin (2017) Improving gastric cancer outcome prediction using single time-point artificial neural network models. Cancer Informatics, 16. pp. 1-11. ISSN 1176-9351 https://journals.sagepub.com/doi/full/10.1177/1176935116686062?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed 10.1177/1176935116686062 |
| spellingShingle | Dezfouli, Hamid Nilsaz Abu Bakar, Mohd Rizam Arasan, Jayanthi Adam, Mohd Bakri Pourhoseingholi, Mohamad Amin Improving gastric cancer outcome prediction using single time-point artificial neural network models |
| title | Improving gastric cancer outcome prediction using single time-point artificial neural network models |
| title_full | Improving gastric cancer outcome prediction using single time-point artificial neural network models |
| title_fullStr | Improving gastric cancer outcome prediction using single time-point artificial neural network models |
| title_full_unstemmed | Improving gastric cancer outcome prediction using single time-point artificial neural network models |
| title_short | Improving gastric cancer outcome prediction using single time-point artificial neural network models |
| title_sort | improving gastric cancer outcome prediction using single time-point artificial neural network models |
| url | http://psasir.upm.edu.my/id/eprint/62125/ http://psasir.upm.edu.my/id/eprint/62125/ http://psasir.upm.edu.my/id/eprint/62125/ http://psasir.upm.edu.my/id/eprint/62125/1/Improving%20gastric%20cancer.pdf |