Implementation of KNN Algorithm with BOA to Predict the Cancer with more Accurate Way

Abstract

K-nearest Neighbor (KNN) is one of the most widely used ML (Machine Learning) methods for data includes organizational, and categorizing illnesses and faults. This is important due to frequent changes in the training sample, for which it would be costly to create a new classifier using most methods each time. As a result, KNN may be employed successfully since it does not need the creation of a residual classifier in before. KNN is simple to use and has a wide range of application possibilities. Here, an unique KNN classification method is proposed that optimizes utilizing the Bayesian Optimization Algorithm (BOA). In order to exploit knowledge about the dataset’s architecture and the cosine similarity of distance, this study proposes changes to the closest neighbour K value in an effort to improve classification accuracy. The results of experimental work based on datasets from the University of California Irvine (UCI) repository indicate enhanced classifier performance relative to traditional KNN and increased reliability without a substantial speed penalty.