KNN and XGBoost Algorithms for Lung Cancer Prediction


  • M. Rhifky Wayahdi Battuta University
  • Fahmi Ruziq Battuta University


K-nearest neighbor algorithm, XGBoost algorithm, Machine learning, Classification algorithms, Cancer prediction.


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.


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How to Cite

M. Rhifky Wayahdi, & Fahmi Ruziq. (2022). KNN and XGBoost Algorithms for Lung Cancer Prediction. Journal of Science Technology (JoSTec), 4(1), 179–186.