Thyroid disease diagnosis using Artificial Immune Recognition System (AIRS)

Authors:
Halife Kodaz;İsmail Babaoğlu;Hazim İşcan
Affiliations:
Selcuk University, Konya, Turkey;Selcuk University, Konya, Turkey;Selcuk University, Konya, Turkey
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

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Abstract

The use of artificial intelligence methods in medical diagnosis is increasing gradually. The effectiveness of classification and recognition systems has improved in a great deal to help medical experts in diagnosing diseases. Artificial Immune Systems (AIS) is a new but effective branch of artificial intelligence. This study aims at diagnosing thyroid disease with Artificial Immune Recognition System (AIRS). Thyroid disease diagnosis is an important classification problem. The thyroid data employed in this study is available from UCI Repository site. This data set is a very commonly used data set in the literature relating the use of classification systems for thyroid disease diagnosis and it was used in this study to compare the classification performance of AIRS with regard to other studies. We obtained a classification accuracy of 94.82%, which is one of the highest accuracies reached so far. This result ensured that AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.