Performance Analysis Of Fuzzy Rough Set-Based And Correlation-Based Attribute Selection Methods On Detection Of Chronic Kidney Disease With Various Classifiers
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Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Technological developments generally have positive effects on our daily lives especially on health domain. Diagnosing diseases through new machines or methods are easier than compared to the past. Benchmarking the effect of attribute selection methods on the performance of classification algorithms in a study to diganose the chronic kidney disease (CKD) by using classification algorithms are aimed. Data set on CKD taken from the UCI machine learning repository has been used for the experiments. After a variety of pre-processing, normalization and attribute selection processes, classifier models are designed. In order to determine the attributes that have gerater contribution on the classification results, the Correlation Based attribute selection (CBAS) method and Fuzzy Rough Set Based attribute selection (FRSBAS) method were used. Two data sets obtained by each attribute selection method and the raw data are classified by 4 classifiers including k-Nearest Neighbor, Navie Bayes, Random Forest and Logistic Regression. The test and training data are separated by 5-fold cross validation. The accuracy, precision, sensitivity, ROC curve and F-measure parameters obtained from confusion matrix are used to compare and evaluate the results of the models. As a result of the study, it is seen that the application of FRSBAS method on CKD data set performs better in all classification algorithms.
Açıklama
International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEY
BASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169
WOS: 000491430200016
BASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169
WOS: 000491430200016
Anahtar Kelimeler
Fuzzy logic, classification, chronic kidney disease, data mining, attribute selection
Kaynak
2019 Scientific Meeting On Electrical-Electronics & Biomedical Engineering And Computer Science (Ebbt)
WoS Q Değeri
N/A
