NDLI: Heart disease diagnosis using extreme learning based neural networks

Content Provider	IEEE Xplore Digital Library
Author	Fathurachman, M. Kalsum, U. Safitri, N. Utomo, C.P.
Copyright Year	2014
Description	Author affiliation: YARSI E-Health Res. Center, Univ. of YARSI, Jakarta, Indonesia (Fathurachman, M.; Kalsum, U.; Safitri, N.; Utomo, C.P.)
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.
Starting Page	23
Ending Page	27
File Size	257490
Page Count	5
File Format	PDF
ISBN	9781479969845
e-ISBN	9781479951000
DOI	10.1109/ICAICTA.2014.7005909
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Support vector machines Training Accuracy Extreme learning machine Heart disease Artificial neural networks Medical diagnosis Diseases Testing
Content Type	Text
Resource Type	Article

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