The medical applications of attribute weighted artificial immune system (AWAIS): diagnosis of heart and diabetes diseases

  • Authors:

  • Seral Şahan;Kemal Polat;Halife Kodaz;Salih Güneş

  • Affiliations:

  • Eng.-Arch. Fac. Electrical & Electronics Eng., Selcuk University, Konya, Turkey;Eng.-Arch. Fac. Electrical & Electronics Eng., Selcuk University, Konya, Turkey;Eng.-Arch. Fac. Computer Eng., Selcuk University, Konya, Turkey;Eng.-Arch. Fac. Electrical & Electronics Eng., Selcuk University, Konya, Turkey

  • Venue:
  • ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
  • Year:
  • 2005

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Abstract

In our previous work, we had been proposed a new artificial immune system named as Attribute Weighted Artificial Immune System (AWAIS) to eliminate the negative effects of taking into account of all attributes in calculating Euclidean distance in shape-space representation which is used in many network-based Artificial Immune Systems (AISs). This system depends on the weighting attributes with respect to their importance degrees in class discrimination. These weights are then used in calculation of Euclidean distances. The performance analyses were conducted in the previous study by using machine learning benchmark datasets. In this study, the performance of AWAIS was investigated for real world problems. The used datasets were medical datasets consisting of Statlog Heart Disease and Pima Indian Diabetes datasets taken from University of California at Irvine (UCI) Machine Learning Repository. Classification accuracies for these datasets were obtained through using 10-fold cross validation method. AWAIS reached 82.59% classification accuracy for Statlog Heart Disease while it obtained a classification accuracy of 75.87% for Pima Indians Diabetes. These results are comparable with other classifiers and give promising performance to AWAIS for that kind of problems.