DocumentCode :
1793561
Title :
Heart disease diagnosis using extreme learning based neural networks
Author :
Fathurachman, Muhammad ; Kalsum, Umi ; Safitri, Noviyanti ; Utomo, Chandra Prasetyo
Author_Institution :
YARSI E-Health Res. Center, Univ. of YARSI, Jakarta, Indonesia
fYear :
2014
fDate :
20-21 Aug. 2014
Firstpage :
23
Lastpage :
27
Abstract :
Heart disease is the leading cause of death in Indonesia based on 2010 Hospital Information System (SIRS) Report. Early detection and treatment of heart disease will reduce the patient mortality rate. Therefore, implementation of artificial neural networks (ANN) technique in diagnosing heart disease have been widely used and reached good accuracy. Beside of that, there are disadvantages in implementation of ANN technique, such as a long training process, many parameters have to be tuned, the obtained solution potentially get stuck in local minima, and activation function must be differentiable. We implemented Extreme Learning Machine (ELM) which is fast, simple tuning, and better generalization model learning algorithm. It has better performance than backpropagation ANN, Support Vector Machine (SVM), and decision tree. The results indicate that the ELM model has potentially implemented to help medical professional in diagnosing heart disease.
Keywords :
learning (artificial intelligence); medical diagnostic computing; neural nets; patient treatment; 2010 Hospital Information System report; ANN technique; ELM model; SIRS report; SVM; activation function; artificial neural networks technique; backpropagation; decision tree; extreme learning machine; generalization model; heart disease diagnosis; heart disease treatment; local minima; medical professional; support vector machine; Accuracy; Artificial neural networks; Diseases; Heart; Support vector machines; Testing; Training; artificial neural networks; extreme learning machine; heart disease; medical diagnosis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014 International Conference of
Conference_Location :
Bandung
Print_ISBN :
978-1-4799-6984-5
Type :
conf
DOI :
10.1109/ICAICTA.2014.7005909
Filename :
7005909
Link To Document :
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