Modeling Stroke Diagnosis with the Use of Intelligent Techniques

Authors:
S. Lalas;N. Ampazis;Athanasios Tsakonas;Georgios Dounias;K. Vemmos
Affiliations:
Department of Financial and Management Engineering, University of the Aegean, Chios, Greece;Department of Financial and Management Engineering, University of the Aegean, Chios, Greece;Department of Financial and Management Engineering, University of the Aegean, Chios, Greece;Department of Financial and Management Engineering, University of the Aegean, Chios, Greece;Unit of Acute Stroke, Therapeutic Clinic, "Alexandra" General Hospital, Athens, Greece
Venue:
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Year:
2008

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Abstract

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.