Prediction of diagnosis in patients with early arthritis using a combined Kohonen mapping and instance-based evaluation criterion

  • Authors:

  • B Wyns;S Sette;L Boullart;D Baeten;I. E. A Hoffman;F De Keyser

  • Affiliations:

  • Department of Electrical Energy, Systems and Automation, Faculty of Applied Sciences, Ghent University, Technologiepark 913, 9000 Ghent, Belgium;Department of Textiles, Faculty of Applied Sciences, Ghent University, Technologiepark 913, 9000 Ghent, Belgium;Department of Electrical Energy, Systems and Automation, Faculty of Applied Sciences, Ghent University, Technologiepark 913, 9000 Ghent, Belgium;Department of Rheumatology, Faculty of Medicine, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium;Department of Rheumatology, Faculty of Medicine, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium;Department of Rheumatology, Faculty of Medicine, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2004

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Abstract

Rheumatoid arthritis (RA) and spondyloarthropathy (SpA) are the two most frequent forms of chronic autoimmune arthritis. These diseases lead to important inflammatory symptoms resulting in an important functional impairment. This paper introduces a self-organizing artificial neural network combined with a case-based reasoning evaluation criterion to predict diagnosis in patients with early arthritis. Results show that 47.2% of the sample space can be predicted with an accuracy of 84.0% and attaining a high confidence level. 37.7% of the sample space is classified with an overall accuracy of 65.0%. The remaining group was labeled as ''undetermined''. A general prediction accuracy of 75.6% is reached, exceeding the performance of other approaches such as a backpropagation neural network and the Quest decision tree program. Furthermore, by using this new method, more specifically case-based reasoning, as a helpful tool to classify patients with early arthritis, the possibility of a confidence measure is given, indicating a degree of ''belief'' of the system in its results. This is often an important feature when dealing with diagnosis in human patients.