TY - GEN
T1 - A clustering approach in developing prognostic systems of cancer patients
AU - Chen, Dechang
AU - Henson, Donald
AU - Schwartz, Arnold M.
AU - Xing, Kai
AU - Sheng, Li
AU - Cheng, Xiuzhen
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=60649088849&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2008.40
DO - 10.1109/ICMLA.2008.40
M3 - Conference contribution
AN - SCOPUS:60649088849
SN - 9780769534954
T3 - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
SP - 723
EP - 728
BT - Proceedings - 7th International Conference on Machine Learning and Applications, ICMLA 2008
T2 - 7th International Conference on Machine Learning and Applications, ICMLA 2008
Y2 - 11 December 2008 through 13 December 2008
ER -