Lion Optimized K-Means Ensemble based Automated Diagnosis of Breast Cancer

Abstract

Breast Cancer is one of the major causes of death among women. Although it is a life threatening medical condition; if diagnosed and treated at earlier stages, the outcome of the patients can be remarkably enhanced. A lot of machine learning techniques have been employed for its proper classification. In this paper, a novel classification algorithm is proposed to classify the tumors as benign or malignant with greater accuracy and tested on Wisconsin Diagnosis Breast Cancer (WDBC) dataset. The proposed model is a weighted ensemble of four k-means classifiers with different values of parameter k and the weights are optimized using Lion optimization technique. The model accounts for an accuracy of 98.24% and performs much better than previous models.