Classification of patients with heart failure

Author

Bayrak, Tuncay ; Ogul, Hasan

Author_Institution

Bilgisayar Muhendisligi Bolumu, Baskent Univ., Ankara, Turkey

fYear

2014

fDate

16-17 Oct. 2014

Firstpage

1

Lastpage

4

Abstract

Echocardiography is imaging of anatomy and physiology of heart with high frequency sound waves by using ultrasonic transducers. The signals obtained by using this method are defined as echocardiogram. In this way, the function of heart can be investigated and any abnormal case is determined according to many parameters. In this study, the classification was realized, according to 7 of features obtained from echocardiogram signals belong to 74 of patient in Machine Learning Repository (UCI) database. Naive Bayes was determined as the best classification method for this dataset and 63% sensitivity, 84% specificity, and an accuracy value of 77% has been reached. In conclusion, this study presents an investigation of determination of which features are significant in death based on heart failure.

Keywords

Bayes methods; biomedical transducers; echocardiography; feature extraction; learning (artificial intelligence); medical signal processing; signal classification; ultrasonic transducers; dataset; echocardiogram signals; echocardiography; feature classification; heart anatomy; heart failure; heart physiology; high-frequency sound waves; machine learning repository database; naive Bayes database; patient classification; ultrasonic transducers; Abstracts; Classification algorithms; Echocardiography; Educational institutions; Heart; Indexes;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering Meeting (BIYOMUT), 2014 18th National

Conference_Location

Istanbul

Type

conf

DOI

10.1109/BIYOMUT.2014.7026353

Filename

7026353