Title :
Classification of patients with heart failure
Author :
Bayrak, Tuncay ; Ogul, Hasan
Author_Institution :
Bilgisayar Muhendisligi Bolumu, Baskent Univ., Ankara, Turkey
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;
Conference_Titel :
Biomedical Engineering Meeting (BIYOMUT), 2014 18th National
Conference_Location :
Istanbul
DOI :
10.1109/BIYOMUT.2014.7026353
