Scopus Eşleşmesi Bulundu
61
Atıf
36
Cilt
2901-2907
Sayfa
Scopus Yazarları: Ismail Saritas
Özet
In this study, an artificial neural network (ANN) was developed to determine whether patients have breast cancer or not. Whether patients have cancer or not and if they have its type can be determined by using ANN and BI-RADS evaluation and based on the age of the patient, mass shape, mass border and mass density. Though this system cannot diagnose cancer conclusively, it helps physicians in deciding whether a biopsy is required by providing information about whether the patient has breast cancer or not. Data obtained from 800 patients who were diagnosed with cancer definitively through biopsy. The definitive diagnosis corresponding to each patient and the data from ANN model results were investigated using Confusion matrix and ROC analyses. In the test data of the ANN model that was implemented as a result of these analyses, disease prediction rate was 90.5% and the health ratio was 80.9%. It is seen from these high predictive values that the ANN model is fast, reliable and without any risks and therefore can be of great help to physicians. © 2011 Springer Science+Business Media, LLC.
Anahtar Kelimeler (Scopus)
Artificial neural network
BI-RADS
Breast cancer
Breast cancer prediction
Scimago Dergi Bilgisi
Otomatik ISSN Eşleştirmesi
2011 yılı verileri
Journal of Medical Systems
Q3
SJR Quartile
0,356
SJR Skoru
120
H-Index
Kategoriler: Health Informatics (Q3) · Health Information Management (Q3) · Information Systems (Q3) · Medicine (miscellaneous) (Q3)
Alanlar: Computer Science · Health Professions · Medicine
Ülke: United States
· Springer New York
Bu bilgiler makale yılına göre Scimago veritabanından ISSN eşleştirmesiyle otomatik getirilmektedir.
Dergi sıralama verileri Scimago'nun ilgili yılı baz alınmaktadır.
Anahtar Kelimeler
Artificial neural network
BI-RADS
Breast cancer
Breast cancer prediction
Makale Bilgileri
Dergi
Journal of Medical Systems
ISSN
0148-5598
Yıl
2011
/ 8. ay
Cilt / Sayı
36
/ 5
Sayfalar
2901 – 2907
Makale Türü
Özgün Makale
Hakemlik
Hakemli
Endeks
SCI-Expanded
JCR Quartile
Q1
YÖKSİS Atıf
20
Yayın Dili
İngilizce
Kapsam
Uluslararası
Toplam Yazar
1 kişi
Erişim Türü
Basılı+Elektronik
Erişim Linki
Makaleye Git
Alan
Mühendislik Temel Alanı
Elektrik-Elektronik ve Haberleşme Mühendisliği
Devreler ve Sistemler Teorisi
Yapay Zeka
YÖKSİS Yazar Kaydı
Yazar Adı
SARITAŞ İSMAİL
YÖKSİS ID
799744
Hızlı Erişim
Metrikler
YÖKSİS Atıf
20
Scopus Atıf
61
JCR Quartile
Q1
Yazar Sayısı
1