K-NN Classifier Analysis for an Epidemic Study on Fatigue Syndrome of Juvenile Educatees

Two contrasting approaches toward an epidemic study were illustrated in this study; one is the regression analysis which is rather conventional methodology used in the past/present epidemic studies, and the other is the classifier analysis which is in the soft computing toolbox. The dataset analysed is a part of a cohort study which principally focused on a fatigue syndrome of the elementary and junior high school educatees. In the classifier analysis we employed a major supervised machine-learning algorithm, K-nearest Neighbour (K-NN), coupled with Principal Component Analysis (PCA). As a result, the performance that was found by the classifier analysis provides rather better results than that of the regression analysis. Finally we discussed the availability of both analyses with referring to the technical and conceptual limitation of both approaches.