Finding Survival Groups in SEER Lung Cancer Data

This paper investigates application of novel Bidirectional Data Partitioning Technique (BDP) to cancer survival analysis. Author has developed this technique for classification problems with unstable feature relevance and SEER Cancer Data illustrates this machine learning concept. BDP is applied for survival analysis in order to find groups of patients with different key factors that determine survival time. BDP operates by weights assigned to instance-feature tuples. A measure of class separability is used as a criterion in finding weights. Weights are then used within clustering in order to find clusters (subgroups of patients) with associated feature importance profiles (scored survival factors). Component models of an ensemble are built after agglomerative merging of subgroups. Because different clustering techniques typically yield different results, accuracy of an ensemble is used to establish final groups. Factors of survival time that are crucial in different situations define survival groups according to a dissimilarity principle. The results establish a base for additional epidemiological surveys and studies.