Smart Doctor: Performance of Supervised ART-I Artificial Neural Network for Breast Cancer Diagnoses

Kamal R.  AL-Rawi; Saifaldeen K.  AL-Rawi

doi:10.24996/ijs.2020.61.9.25

Authors

Kamal R. AL-Rawi Computer Science Department, Faculty of Computer Science and Informatics, Amman Arab University, Amman, Jordan
Saifaldeen K. AL-Rawi Department of Computer Engineering, School Electrical Engineering and Information Technology, German Jordanian University, Amman, Jordan

DOI:

https://doi.org/10.24996/ijs.2020.61.9.25

Keywords:

Adaptive Resonance Theory, Artificial Neural Network, Breast Cancer Diagnoses, Machine Learning

Abstract

Wisconsin Breast Cancer Dataset (WBCD) was employed to show the performance of the Adaptive Resonance Theory (ART), specifically the supervised ART-I Artificial Neural Network (ANN), to build a breast cancer diagnosis smart system. It was fed with different learning parameters and sets. The best result was achieved when the model was trained with 50% of the data and tested with the remaining 50%. Classification accuracy was compared to other artificial intelligence algorithms, which included fuzzy classifier, MLP-ANN, and SVM. We achieved the highest accuracy with such low learning/testing ratio.