A novel ensemble strategy for classification of prostate cancer protein mass spectra.

Protein mass spectra pattern recognition is a new forum in which many machine learning algorithms have been conducted to enhance the chance of early cancer diagnosis. The high-dimensionality-small-sample (HDSS) problem of cancer proteomic datasets still requires more sophisticated approaches to improve the classification accuracy. In this study we present a simple ensemble strategy based on measuring the generalizing capability of different subsets of training data and apply it in making final decision.

Classifying depth of anesthesia using EEG features, a comparison.

Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric.