Feature Selection Using Genetic Algorithm for Cancer Prediction System

Abstract

In healthcare sector, cancer is one of the most threatening and fast-growing diseases. The early diagnosis of this disease is very important as the success rate of its treatment depends upon how early and accurately it is diagnosed. The machine learning algorithms are helpful in detection and prediction of diseases. To improve efficiency of these algorithms, optimal features need to be selected. So, this research work uses genetic algorithm to select optimal features before applying k-nearest neighbor (KNN) and weighted k-nearest neighbor (WKNN) on Wisconsin Breast Cancer Prognosis dataset extracted from UCI repository. This approach helps in early prediction and the results show that WKNN performed better with 86.44% accuracy than KNN which gives 83.05% accuracy.