Scopus
YÖKSİS DOI Eşleşti
SJR Q1
A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases
Journal of Medical Systems · Mayıs 2014
YÖKSİS Kayıtları
A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases
Journal of Medical Systems · 2014 SCI-Expanded 8 atıf
Dr. Öğr. Üyesi ONUR İNAN →
A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases
Journal of Medical Systems · 2014 SCI-Expanded
Doç. Dr. MUSTAFA SERTER UZER →
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A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases
2014 ISSN: 0148-5598 SCI-Expanded 8 atıf
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A New Data Preparation Method Based on Clustering Algorithms for Diagnosis Systems of Heart and Diabetes Diseases
2014 ISSN: 0148-5598 SCI-Expanded Q2
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Makale Bilgileri
ISSN01485598
Yayın TarihiMayıs 2014
Cilt / Sayfa38
Scopus ID2-s2.0-84901577686
Özet
The most important factors that prevent pattern recognition from functioning rapidly and effectively are the noisy and inconsistent data in databases. This article presents a new data preparation method based on clustering algorithms for diagnosis of heart and diabetes diseases. In this method, a new modified K-means Algorithm is used for clustering based data preparation system for the elimination of noisy and inconsistent data and Support Vector Machines is used for classification. This newly developed approach was tested in the diagnosis of heart diseases and diabetes, which are prevalent within society and figure among the leading causes of death. The data sets used in the diagnosis of these diseases are the Statlog (Heart), the SPECT images and the Pima Indians Diabetes data sets obtained from the UCI database. The proposed system achieved 97.87 %, 98.18 %, 96.71 % classification success rates from these data sets. Classification accuracies for these data sets were obtained through using 10-fold cross-validation method. According to the results, the proposed method of performance is highly successful compared to other results attained, and seems very promising for pattern recognition applications.
Yazarlar (3)
1
Nihat Yilmaz
2
Onur Inan
3
Mustafa Serter Uzer
ORCID: 0000-0002-8829-5987
Anahtar Kelimeler
Heart and Diabetes diseases
Modified K-means Algorithm
Support Vector Machine
Kurumlar
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Journal of Medical Systems
Q1
SJR Skoru0,962
H-Index120
YayıncıSpringer New York
ÜlkeUnited States
Information Systems (Q1)
Medicine (miscellaneous) (Q1)
Health Informatics (Q2)
Health Information Management (Q2)
Metrikler
60
Atıf
3
Yazar
3
Anahtar Kelime