Scopus
YÖKSİS Eşleşti
Design of a hybrid system for the diabetes and heart diseases
Expert Systems with Applications · Temmuz 2008
YÖKSİS Kayıtları
Design of a hybrid system for the diabetes and heart diseases
Expert Systems with Applications · 2008 SCI-Expanded 85 atıf
PROFESÖR HUMAR KAHRAMANLI ÖRNEK →
Makale Bilgileri
DergiExpert Systems with Applications
Yayın TarihiTemmuz 2008
Cilt / Sayfa35 · 82-89
Scopus ID2-s2.0-44949105491
Özet
Data can be classified according to their properties. Classification is implemented by developing a model with existing records by using sample data. One of the aims of classification is to increase the reliability of the results obtained from the data. Fuzzy and crisp values are used together in medical data. Regarding to this, a new method is presented for classification of data of a medical database in this study. Also a hybrid neural network that includes artificial neural network (ANN) and fuzzy neural network (FNN) was developed. Two real-time problem data were investigated for determining the applicability of the proposed method. The data were obtained from the University of California at Irvine (UCI) machine learning repository. The datasets are Pima Indians diabetes and Cleveland heart disease. In order to evaluate the performance of the proposed method accuracy, sensitivity and specificity performance measures that are used commonly in medical classification studies were used. The classification accuracies of these datasets were obtained by k-fold cross-validation. The proposed method achieved accuracy values 84.24% and 86.8% for Pima Indians diabetes dataset and Cleveland heart disease dataset, respectively. It has been observed that these results are one of the best results compared with results obtained from related previous studies and reported in the UCI web sites. © 2007.
Yazarlar (2)
1
Humar Kahramanli
2
Novruz Allahverdi
Anahtar Kelimeler
Backpropagation
Classification
Cleveland heart disease
Fuzzy neural network
k-fold cross-validation
Pima Indians diabetes
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
333
Atıf
2
Yazar
6
Anahtar Kelime