On machine learning classification of otoneurological data.
A dataset including cases of six otoneurological diseases was analysed using machine learning methods to investigate the classification problem of these diseases and to compare the effectiveness of different methods for this data. Linear discriminant analysis was the best method and next multilayer perceptron neural networks provided that the data was input into a network in the form of principal components. Nearest neighbour searching, k-means clustering and Kohonen neural networks achieved almost as good results as the former, but decision trees slightly worse. Thus, these methods fared well, but Naïve Bayes rule could not be used since some data matrices were singular. Otoneurological cases subject to the six diseases given can be reliably distinguished.
Juhola
Martti
M
Department of Computer Sciences, 33014 University of Tampere, Finland.
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D005103:Expert Systems; D003639:Hearing Loss, Sudden / Q000145:classification* / Q000209:etiology; D016347:Medical Records Systems, Computerized; D008575:Meniere Disease / Q000145:classification* / Q000175:diagnosis; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D009464:Neuroma, Acoustic / Q000145:classification* / Q000175:diagnosis; D014717:Vertigo / Q000145:classification*; D020338:Vestibular Neuronitis / Q000145:classification* / Q000175:diagnosis