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dc.contributor.authorGünay, Melike
dc.contributor.authorOrman, Zeynep
dc.description.abstractThe Artificial Immune System (AIS) is a computational intelligence method inspired from the human immune system, which is applied to real-world problem solving related to classification, optimization and anomaly detection as an alternative approach to many data mining techniques. This paper presents a medical disease prediction system by using the AIS algorithm. The proposed system is implemented and tested on two different datasets which include breast cancer data and heart disease data with four different types of illness. Two other well-known data mining techniques that are Artificial Neural Networks (ANN) and K-Nearest Neighbor (KNN) are also tested on the same datasets to make a comparison in terms of their classification efficiency. By using AIS, accuracy obtained on breast cancer dataset is 98.08% and heart disease dataset is 70%. In addition to this, AIS algorithm gives the best classification results for both datasets. We also analyze the positive effect of preprocessing data before classification. Clearly, decreasing the number of different values that a class can be assigned for multivariate classes and assigning weights to each feature in heart disease dataset give prediction result with higher accuracy.tr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.subjectComputing Methodologiestr_TR
dc.subjectMachine Learningtr_TR
dc.subjectLearning Paradigmstr_TR
dc.subjectBilgi İşlem Metodolojileritr_TR
dc.subjectMakine Öğrenmetr_TR
dc.subjectÖğrenme Paradigmalarıtr_TR
dc.titleDisease Prediction Using Weighted Artificial Immune Systemtr_TR
dc.relation.journal2019 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2019)tr_TR

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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States