Assessing effects of pre-processing mass spectrometry data on classification performance

Abstract

Disease prediction through mass spectrometry (MS) data is gaining importance in medical diagnosis. Particularly in cancerous diseases, early prediction is one of the most life saving stages. High dimension and the noisy nature of MS data requires a two-phase study for successful disease prediction; first, MS data must be pre-processed with stages such as baseline correction, normalizing, de-noising and peak detection. Second, a dimension reduction based classifier design is the main objective. Having the data pre-processed, the prediction accuracy of the classifier algorithm becomes the most significant factor in the medical diagnosis phase. As health is the main concern, the accuracy of the classifier is clearly very important. In this study, the effects of the pre-processing stages of MS data on classifier performances are addressed. Three pre-processing stages-baseline correction, normalization and de-noising-are applied to three MS data samples, namely, high-resolution ovarian cancer, low-resolution prostate cancer and a tow-resolution ovarian cancer. To measure the effects of the pre-processing stages quantitatively, four diverse classifiers, genetic algorithm wrapped K-nearest neighbor (GA-KNN), principal component analysis-based Least discriminant analysis (PCA-LDA), a neural network (NN) and a support vector machine (SVM) are applied to the data sets. Calculated classifier performances have demonstrated the effects of pre-processing stages quantitatively and the importance of pre-processing stages on the prediction accuracy of classifiers. Results of computations have been shown clearly.