Support vector machine ensembles for breast cancer type prediction from mid-FTIR micro-calcification spectra

Over the past years Fourier transform infrared (FTIR) spectroscopy has been demonstrated as a prospective tool for cancer diagnostics. In order to apply FTIR spectroscopy as a routine tool for biomedical diagnostics of tissue samples, strong and reliable classifiers are needed. Frequently, the number of available tissue samples is restricted and due to that data sets consist of a small number of samples, often less than 100. This can result in unstable classifiers, which perform poorly on unseen data. In this work we present a way to overcome this limitation by aggregating several support vector machines in to an ensemble. Different ensemble systems, including bagging, boosting and tree-based models, were investigated for a FTIR data set acquired from different types and stages of breast cancer. It was found that an ensemble system predicts 88.9% of the unseen multi-class test set correctly. In comparison a single classifier only achieved a predictive performance of 66.7%. As these results show, the application of SVM ensembles in biomedical diagnostics using FTIR spectroscopy can be highly beneficial.