Classification accuracy of a frequency analysis method: comparison between supervised SOM and KNN

In FAM measurement an alternative current stimulus at several frequencies is fed to the human body resulting in physiological responses whose thresholds are recorded. The idea of the FAM is to investigate the physiological properties of the human body by analyzing those thresholds. The basic objective is to make diagnostic classification on the basis of the measured threshold values. In this study properties of two methods, supervised SOM and kNN, are applied to the diagnostic classification task. The classification accuracy of those methods in FAM data analysis is defined and properties of the methods and the data are discussed. The classification accuracy of both methods was about 70% in classification to two classes and this result shows the supervised SOM has about the same performance in accuracy as the kNN has in the classification of the FAM data.