Abstract:
In hospital environments around the world bacterial
contamination is prevalence. One of the most commonly found bacteria is the
Acinetobacter Baumannii. It can cause unitary tract, lung, abdominal and central
nervous system infection. This bacteria is becoming more resistant to antibiotics.
Thus, identification of the non-resistant from the resistant bacteria strain is of
important for the correct course of treatments. We propose to use the artificial
neural network (ANN) for supervised identification of this bacteria. The mass
spectra generated from the liquid chromatography mass spectrometry (LCMS)
were used as the features to train the ANN. However, due to the massive number
of features, we applied the principle component analysis (PCA) to reduce the
dimensions. Less than 1% of the original number of features were utilized. The
hand out validation method confirmed that the accuracy, sensitivity and specificity
are 0.75 respectively. In order to avoid selection biasness in the sampling, 5-fold
cross validation was performed. In comparison, the average accuracy is close to
0.75 but the average sensitivity is slightly higher by 0.50
