Classification of Real Imbalanced Cardiovascular Data Using Feature Selection and Sampling Methods: A Case Study with Neural Networks and Logistic Regression

BEKTAŞ, JALE; İBRİKÇİ, TURGAY; ÖZCAN, İSMAİL

doi:10.1142/s0218213017500191

Classification of Real Imbalanced Cardiovascular Data Using Feature Selection and Sampling Methods: A Case Study with Neural Networks and Logistic Regression

Atıf İçin Kopyala

BEKTAŞ J., İBRİKÇİ T., ÖZCAN İ. T.

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, cilt.26, sa.6, 2017 (SCI-Expanded)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 26 Sayı: 6
Basım Tarihi: 2017
Doi Numarası: 10.1142/s0218213017500191
Dergi Adı: INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Çukurova Üniversitesi Adresli: Evet

Özet

Coronary Artery Disease (CAD) is very common among the major types of cardiovascular diseases, and there are several studies created with different features including data that is collected from patients for timely diagnosis of CAD.