Building an Effective Classification Model for Breast Cancer Patient Response Data

Choosing an appropriate cancer treatment is potentially the most important task in the treatment of a cancer patient. If it were possible to identify the best option for a patient (or at minimum to remove options that will not help the patient), then the general prognosis of the patient improves. However, this task becomes much more subtle due to characteristics such as high dimensionality found in many gene expression datasets. In this study, we seek to identify classifiers and feature selection techniques best suited for predicting a breast cancer patient´s response to a cancer treatment. In order to determine this, we have collected a group of five high-dimensional breast cancer patient response datasets and use a group of four classifiers, and three feature selection techniques along with four feature subset sizes. Our results show that 5-Nearest Neighbor classifier and Signal-to-Noise feature selection technique are the most frequently top performing techniques. Statistical analysis confirms that these techniques are the top performing techniques. Thus, we recommend the use of 5-Nearest Neighbor and Signal-to-Noise for breast cancer patient response data. To our knowledge, this is the first study that focuses on the classification process on patient response data for breast cancer.