Modelling of Oral Cancer Progression Using Dynamic Bayesian Networks

Abstract

This work addresses the problem of finding the mortality distribution for lung cancer in Mexican districts, through clustering patterns discovery. A data mining system was developed which consists of a pattern generator and a visualization subsystem. Such an approach may contribute to biomarker discovery by means of identifying risk regions for a given cancer type and further reduce the cost and time spend in conducting cancer studies. The k-means algorithm was used for the generation of patterns, which permits expressing patterns as groups of districts with affinity in their location and mortality rate attributes. The source data were obtained from Mexican official institutions. As a result, a set of grouping patterns reflecting the mortality distribution of lung cancer in Mexico was generated. Two interesting patterns in northeastern and northwestern Mexico with high mortality rate were detected. We consider that patterns generated by the data mining system, can be useful for identifying high risk cancer areas and biomarkers discovery.