KNN and XGBoost Algorithms for Lung Cancer Prediction

Abstract

In this paper, the K-Nearest Neighbor and XGBoost algorithms will be implemented in lung cancer prediction. This prediction is important because lung cancer is one of the highest causes of death worldwide. Prediction and diagnosis of this cancer can be done with machine learning algorithms such as K-Nearest Neighbor and XGBoost. Based on the results of the analysis and testing of the K-Nearest Neighbor algorithm and the XGBoost algorithm, the results show that the two algorithms obtain a very good level of accuracy, as well as obtain a balanced precision, recall, and f1-score. But in this case the XGBoost algorithm tends to be better than the KNN algorithm in recognizing a given data pattern.