International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Mining of Epigenetic Data for the Effective Prediction of Bladder Cancer

Author : Mohammed Siyad B ¹

Date of Publication :7th January 2017

Abstract: Epigenetic alterations have been associated with a wide variety of diseases including cancer. Bladder cancer is the fourth most common cancer and the ninth driving reason of cancer death. A lot of tools and protocols have been developed for the diagnosis of bladder cancer over the past 5 to 10 years. In this paper, a machine learning approach is proposed for effectively predicting the disease from epigenetic information in the context of bladder cancer. Three different feature selection methods were assessed in combination with three classification methods, using 10-fold cross-validation on the training data set. A model consisting of 151 genes(treated as features) selected through genetic algorithm and random forest classification is identified as the best model with AUC=0.96 from 10-fold cross validation. Most of the selected genes which formed the basis of prediction were allegedly reported in the pathways related to bladder cancer. Hence the best selected model can be effectively applied for better disease diagnosis and prognosis.

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