Conference proceeding
A clustering-based approach to predict outcome in cancer patients
ICMLA 2007: SIXTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, pp 541-546
01 Jan 2007
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The TNM (Tumor, Lymph Node, Metastasis) is a widely used staging system for predicting the outcome of cancer patients. However, the TNM is not accurate in prediction, partially due to the fact of deficient staging within and between stages. Based on the availability of large cancer patient datasets, there is a need to expand the TNM. In this paper, we present a general clustering-based approach to accomplish this task of expansion. This approach admits multiple factors. One major advantage of the approach is that patients within each generated group are homogeneous in terms of survival, so that a more accurate prediction of outcome of patients can be made. A demonstration of use of the proposed method is given for breast cancer patients.
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Details
- Title
- A clustering-based approach to predict outcome in cancer patients
- Creators
- Kai Xing - George Washington Univ, Dept Comp Sci, Washington, DC 20052 USADechang Chen - Uniformed Serv Univ Hlth Sci, Div Epidemiol & Biostat, Bethesda, MD 20814 USADonald Henson - George Washington Univ Cancer Inst, Washington, DC 20037 USALi Sheng - Drexel University
- Contributors
- M A Wani (Editor)M M Kantardzic (Editor)T Li (Editor)Y Liu (Editor)L Kurgan (Editor)J Ye (Editor)M Ogihara (Editor)S Sagiroglu (Editor)X W Chen (Editor)L Peterson (Editor)K Hafeez (Editor)
- Publication Details
- ICMLA 2007: SIXTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, pp 541-546
- Conference
- ICMLA 2007: SIXTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, 6th
- Publisher
- IEEE
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000252793400088
- Scopus ID
- 2-s2.0-47349108586
- Other Identifier
- 991019168976604721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic