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Modeling Stroke Diagnosis with the Use of Intelligent Techniques.

Lalas, S., Ampazis , N., Tsakonas, A., Dounias, G. and Vemmos, K., 2008. Modeling Stroke Diagnosis with the Use of Intelligent Techniques. In: Darzentas , J., Vouros , G., Vosinakis , S. and Arnellos, A., eds. Artificial Intelligence: Theories, Models and Applications: 5th Hellenic Conference on AI, SETN 2008, Syros, Greece, October 2-4, 2008, Proceedings. Berlin-Heidelberg: Springer-Verlag, 352-358.

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DOI: 10.1007/978-3-540-87881-0_32


The purpose of this work is to test the efficiency of specific intelligent classification algorithms when dealing with the domain of stroke medical diagnosis. The dataset consists of patient records of the ”Acute Stroke Unit”, Alexandra Hospital, Athens, Greece, describing patients suffering one of 5 different stroke types diagnosed by 127 diagnostic attributes / symptoms collected during the first hours of the emergency stroke situation as well as during the hospitalization and recovery phase of the patients. Prior to the application of the intelligent classifier the dimensionality of the dataset is further reduced using a variety of classic and state of the art dimensionality reductions techniques so as to capture the intrinsic dimensionality of the data. The results obtained indicate that the proposed methodology achieves prediction accuracy levels that are comparable to those obtained by intelligent classifiers trained on the original feature space.

Item Type:Book Section
Series Name:Lecture Notes in Computer Science
Number of Pages:444
Group:Faculty of Science & Technology
ID Code:17862
Deposited By: Dr Athanasios Tsakonas LEFT
Deposited On:25 May 2011 14:34
Last Modified:14 Mar 2022 13:38

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