Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification
| Main Authors: | Abu Yazid, Mohamad Haider; Universiti Teknologi Malaysia (UTM), Satria, Haikal; Universiti Teknologi Malaysia, Talib, Shukor; Unversiti Teknologi Malaysia, Azman, Novi; Universitas Nasional & Universiti Teknikal Malaysia Melaka |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
IAES Indonesia Section
, 2019
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| Subjects: | |
| Online Access: |
http://journal.portalgaruda.org/index.php/EECSI/article/view/1695 http://journal.portalgaruda.org/index.php/EECSI/article/view/1695/1167 |
Daftar Isi:
- Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset.