Neural network classification of otoneurological data and its visualization

Authors:
Markku Siermala;Martti Juhola;Erna Kentala
Affiliations:
Institute of Medical Technology, 33014 University of Tampere, Tampere, Finland;Department of Computer Sciences, 33014 University of Tampere, Tampere, Finland;Department of Otorhinolaryngology, Helsinki University Central Hospital, Helsinki, Finland
Venue:
Computers in Biology and Medicine
Year:
2008

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Abstract

When complex data is distributed in a biased manner between disease classes, classification accuracy can be increased with a network set of perceptron neural networks developed by us. A novel projection method is also introduced for the visual classification of the data to elucidate its features and disease class distribution. The set of the perceptron neural networks and the projection method were tested with otoneurological data and they improved average sensitivity and positive predictive value at least 10% up to 85% and 83%, compared to our earlier neural network classifications with the same data. The methods were also experimented with two additional data sets, which included diagnostically very difficult cases.