A modified-weighted-k-nearest neighbour and cuckoo search hybrid model for breast cancer classification

Abstract

One of the leading death-causing cancers in women is Breast Cancer. Accurate, precise, and early diagnosis is a crucial solution to survival. Data mining techniques have proved to produce good results in disease diagnosis. Feature search techniques are useful in identifying the relevant features for classification thus reducing time and effort. Class inequality is a significant challenge and one of the methods to overcome it is class balancing. In certain cases, the negative class is the majority class. To be specific; the negative class has a more number of instances than the positive class, so the overall classifier performance may be high; consequently, the classifier performance in accurately identifying positive instances gets overlooked. In this paper, a combination of two class balancing approaches is applied. It is used to balance the number of instances in each of the target classes. k-Nearest Neighbour classifier is a simple, easy to implement, and robust classifier with few parameters needed to be tuned. In this paper, we propose a k-Nearest Neighbour Classifier model implemented with feature search using Cuckoo search and Class balancing to classify Breast Cancer. The proposed model produced an accuracy of 99.41 %., ROC of 0.999, and MCC of 0.988.