A Clustering Approach in Developing Prognostic Systems of Cancer Patients

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 clu...

• Type: Conference
• Year: 2008
• Where: ICMLA
• Authors: Dechang Chen, Kai Xing, Donald Henson, Li Sheng, Arnold M. Schwartz, Xiuzhen Cheng
• Keywords: Cancer Patients, ICMLA 2008, Machine Learning, Prediction, Survival Prediction