Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Mohamad Haider Abu Yazid (Universiti Teknologi Malaysia (UTM))
Haikal Satria (Universiti Teknologi Malaysia)
Shukor Talib (Unversiti Teknologi Malaysia)
Novi Azman (Universitas Nasional & Universiti Teknikal Malaysia Melaka)

Proceeding of the Electrical Engineering Computer Science and Informatics
Vol 5: EECSI 2018

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.