Conference proceeding
A Clustering Approach in Developing Prognostic Systems of Cancer Patients
SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, pp 723-728
01 Jan 2008
Abstract
Accurate Prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, mainly because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present a clustering based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data Partitions produced by multiple clusterings. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster Our approach admits multiple factors and provides a practical and useful toot in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients.
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Details
- Title
- A Clustering Approach in Developing Prognostic Systems of Cancer Patients
- Creators
- Dechang Chen - Uniformed Services University of the Health SciencesKai Xing - George Washington UniversityDonald Henson - George Washington UniversityLi Sheng - Drexel UniversityArnold M. Schwartz - George Washington UniversityXiuzhen Cheng - George Washington University
- Publication Details
- SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, PROCEEDINGS, pp 723-728
- Conference
- SEVENTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, 7th (San Diego, California, United States, 11 Dec 2008–13 Dec 2008)
- Publisher
- IEEE
- Number of pages
- 2
- Grant note
- CCF-0729080 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000263205800108
- Scopus ID
- 2-s2.0-60649088849
- Other Identifier
- 991019169515804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic