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

Copy For Citation

BEKTAŞ J., İBRİKÇİ T., ÖZCAN İ. T.

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, vol.26, no.6, 2017 (SCI-Expanded)

Publication Type: Article / Article
Volume: 26 Issue: 6
Publication Date: 2017
Doi Number: 10.1142/s0218213017500191
Journal Name: INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Çukurova University Affiliated: Yes

Abstract

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.