Prediction of labor for pregnant women using high-resolution mass spectrometry data

High-resolution MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) mass spectrometry has shown promise as a screening tool for detecting discriminatory protein patterns. The major computational obstacle in analyzing MALDI-TOF data is a large number of mass/charge peaks (a.k.a. features, data points). With the number of data points easily going beyond one million for a single sample, efficient feature selection is critical for unequivocal protein pattern discovery. To tackle this problem, we have developed a multi-step strategy for data preprocessing and afterwards feature selection. The preprocessing is composed of binning, baseline correction, and normalization. For the preprocessed data, we propose a new feature subset selection method that is a hybrid filter/wrapper approach. Based on the two feature subsets for each feature, high and low correlated subsets, a feature is assigned a weight which indicates the extent of feature importance. Our scheme is applied to the analysis of labor dataset to predict delivery time of pregnant women. To validate the performance of the proposed algorithm, experiments are performed in comparison with other feature selection and classification methods. We show that our proposed approach outperforms other algorithms