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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Mohamad Haider Abu Yazid
Universiti Teknologi Malaysia (UTM)
Computer Science & IT Electrical & Electronics Engineering

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Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification Mohamad Haider Abu Yazid; Haikal Satria; Shukor Talib; Novi Azman
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.041 KB) | DOI: 10.11591/eecsi.v5.1695

Abstract

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.
Co-Authors Haikal Satria Novi Azman Shukor Talib