Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Mohamad Haider Abu Yazid, Haikal Satria, Shukor Talib, Novi Azman


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.


artificial neural network; heart disease classification; artificial neural network parameter tuning; statlog heart dataset; cleveland heart dataset

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