| dc.description.abstract | Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in the identification of microorganisms and applied for the prediction of antibiotic resistance in recent years. In order to distinguish antibiotic resistant bacteria, various preprocessing methods are used to find informative peaks from the MS data. Using different preprocessing methods will get different information. Get more informative peaks from spectra to promote the performance on identification of antibiotic resistance. In this study, we combine multiple preprocessing methods, FlexAnalysis (Bruker Daltonics), MALDIquant (R package), and continuous wavelet transform-based method, to detect peaks and build machine learning classifiers, logistic regressions, naïve Bayes classifiers, random forests and support vector machine, to identify antibiotic resistance for Acinetobacter nosocomialis, Acinetobacter baumannii, Enterococcus faecium, Group B Streptococci based on the MS data provided by Chang Gung Memorial Hospital. Meanwhile, the combined method will be compared with the individual method. The random forest with the combined methods have the highest accuracy and achieve 90.96%, 84.37%, 78.54% and 70.12% accuracy on independent test respectively. Through feature selection, important peaks about antibiotic resistance could be found from the integrated information. The prediction model can provide an opinion for clinicians, and the informative peak can provide a reference for further research. | en_US |