Polat, KemalGüneş, SalihTosun, Sülayman2020-03-262020-03-262006Polat, K., Güneş, S., Tosun, S., (2006). Diagnosis of Heart Disease Using Artificial Immune Recognition System and Fuzzy Weighted Pre-Processing. Pattern Recognition, (39), 2186-2193. Doi: 10.1016/j.patcog.2006.05.0280031-3203https://dx.doi.org/10.1016/j.patcog.2006.05.028https://hdl.handle.net/20.500.12395/20432This paper presents a novel method for diagnosis of heart disease. The proposed method is based on a hybrid method that uses fuzzy weighted pre-processing and artificial immune recognition system (AIRS). Artificial immune recognition system has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, liver disorders classification. The robustness of the proposed method is examined using classification accuracy, k-fold cross-validation method and confusion matrix. The obtained classification accuracy is 96.30% and it is very promising compared to the previously reported classification techniques.en10.1016/j.patcog.2006.05.028info:eu-repo/semantics/openAccessheart diseaseartificial immune systemAIRSFuzzy weighted pre-processingk-fold cross validationmedical diagnosisArticle3921862193Q1WOS:000240156500021Q1