Ensemble learning of colorectal cancer survival rates
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build...
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| Format: | Conference or Workshop Item |
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2013
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| Online Access: | https://eprints.nottingham.ac.uk/3344/ |
| _version_ | 1848801177158811648 |
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| author | Roadknight, Chris Aickelin, Uwe Scholefield, John Durrant, Lindy |
| author_facet | Roadknight, Chris Aickelin, Uwe Scholefield, John Durrant, Lindy |
| author_sort | Roadknight, Chris |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. |
| first_indexed | 2025-11-14T18:21:38Z |
| format | Conference or Workshop Item |
| id | nottingham-3344 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T21:03:18Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33442025-09-09T14:57:22Z https://eprints.nottingham.ac.uk/3344/ Ensemble learning of colorectal cancer survival rates Roadknight, Chris Aickelin, Uwe Scholefield, John Durrant, Lindy In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. We build on existing research on clustering and machine learning facets of this data to demonstrate a role for an ensemble approach to highlighting patients with clearer prognosis parameters. Results for survival prediction using 3 different approaches are shown for a subset of the data which is most difficult to model. The performance of each model individually is compared with subsets of the data where some agreement is reached for multiple models. Significant improvements in model accuracy on an unseen test set can be achieved for patients where agreement between models is achieved. 2013-09 Conference or Workshop Item PeerReviewed Roadknight, Chris, Aickelin, Uwe, Scholefield, John and Durrant, Lindy (2013) Ensemble learning of colorectal cancer survival rates. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2013, 15-17 July 2013, Milan, Italy. Biomedical Informatics http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6617400 |
| spellingShingle | Biomedical Informatics Roadknight, Chris Aickelin, Uwe Scholefield, John Durrant, Lindy Ensemble learning of colorectal cancer survival rates |
| title | Ensemble learning of colorectal cancer survival rates |
| title_full | Ensemble learning of colorectal cancer survival rates |
| title_fullStr | Ensemble learning of colorectal cancer survival rates |
| title_full_unstemmed | Ensemble learning of colorectal cancer survival rates |
| title_short | Ensemble learning of colorectal cancer survival rates |
| title_sort | ensemble learning of colorectal cancer survival rates |
| topic | Biomedical Informatics |
| url | https://eprints.nottingham.ac.uk/3344/ https://eprints.nottingham.ac.uk/3344/ |