Improving classification accuracy of response in leukaemia treatment using feature selection over pathway segmentation

Abstract

Motivation: Many people die every year from leukaemia. Some of them respond to treatment, and some of them not. This study investigates whether there is any relationship between response to treatment and features drawn from the measured methylation profiles of a set of patients. Such features could potentially be used to predict the outcome of a putative treatment regime. Results: Using AdaBoost with decision trees as weak classifiers, we managed to identify two pathways that affect classification of response and progression in blood cancer with 0.988 accuracy. We also identified a gene whose presence or absence from the dataset can drop classification accuracy from 0.988 to random. Conclusion: We identified one gene that with 99% accuracy can predict response to treatment. We were also able to identify a list of genes from the same dataset that can predict response with 0.94% accuracy.