NDLI: A Clustering Approach in Developing Prognostic Systems of Cancer Patients

Content Provider	IEEE Xplore Digital Library
Author	Dechang Chen Kai Xing Henson, D. Li Sheng Schwartz, A.M. Xiuzhen Cheng
Copyright Year	2008
Description	Author affiliation: Dept. of Math., Drexel Univ. Philadelphia, Philadelphia, PA, USA (Li Sheng) \|\| Dept. of Comput. Sci., George Washington Univ., Washington, DC, USA (Xiuzhen Cheng) \|\| Dept. of Comput. Sci., George Washington Univ. Washington, Washington, DC, USA (Kai Xing) \|\| Dept. of Pathology, George Washington Univ. Med. Center, Washington, DC, USA (Schwartz, A.M.) \|\| Div. of Epidemiology & Biostat., Uniformed Services Univ., Bethesda, MD, USA (Dechang Chen) \|\| George Washington Univ. Cancer Inst., Washington, DC, USA (Henson, D.)
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 tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients.
Starting Page	723
Ending Page	728
File Size	189298
Page Count	6
File Format	PDF
ISBN	9780769534954
DOI	10.1109/ICMLA.2008.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods survival prediction Medical treatment Partitioning algorithms Neoplasms Lymph nodes Computer science lung cancer TNM Lungs Clustering algorithms Machine learning clustering Cancer
Content Type	Text
Resource Type	Article

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