Enhancing Multi-Layer Perceptron for Breast Cancer Prediction

Abstract

Breast cancer (BC) is a standout disease of the most well-known cancers among women around the world. The analysis and prediction of BC leads to early manage the disease and protect the patients from further medical complications. In the light of its noticeable focal points in basic highlights identification from complex BC datasets, Machine Learning (ML) is generally perceived as the technique of decision in BC design order and gauge displaying. Because of the high performance of the Multi-layer Perceptron (MLP) algorithm as one of the ML techniques, we conduct experiments in order to enhance the accuracy rate by tuning its hyper-parameters. The best MLP model is then applied for breast cancer classification using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Then, many methods were used to select superior features. Feature selection results indicated that an increase in the number of input parameters tends to reduce the error associated with the estimator model. The experimental results show an accuracy reached 97.70%.