A Neural Expert System to Identify Major Risk Factors of Breast Cancer
Akhil Kumar Das, Saroj Kr. Biswas, Ardhendu Kumar Mandal, Manomita Chakraborty · 2020 IEEE International Conference for Innovation in Technology (INOCON) · 2020
Now a days Breast Cancer (BC) is a very common cancer among women and rarely man. BC incidence rate keeps increasing since 2000. Many intelligent systems have been proposed using different Machine Learning (ML) Techniques for early diagnosis of BC. Neural Networks (NN) is considered as one of the best ML classification techniques. In this paper we propose a medical expert system which named Transparent Neural Expert System for Breast Cancer (TNESBC) to manage BC by identifying Major Risk factors. The proposed TNESBC adopts the white-box NN model which named “Rule Extraction from Artificial Neural Network(ANN) applying Classified and Misclassified data” (RxNCM) for rule extraction from BC database. The rules are generated in this system which are most transparent to verify the Major Risk factors of BC. The proposed TNESBC algorithm is comparable the RxREN algorithm. The experimental analysis, it is noticed that TNESBC algorithm performs better than RxREN. The comparison is formed using 10-fold Cross-Validation(CV) accuracy, average recall rate, average number of antecedents per rules and average false positive rate.