Combination of Smote and Random Forest Methods for Lung Cancer Classification

  • Christopher Michael Lauw Unuversitas Bumigora
  • Hairani Hairani Universitas Bumigora
  • Ilham Saifuddin Universitas Muhammadiyah Jember
  • Juvinal Ximenes Guterres Universidade Oriental Timur Lorosa’e
  • Muhammad Maariful Huda Politeknik Angkatan Darat
  • Mayadi Mayadi University Teknologi Mara
Keywords: Data Mining, Lung Cancer, Prediction Method, Random Forest


Lung cancer is a network of cells that grow abnormally in the lungs. Lung cancer has four severity levels, namely stages 1 to 4. If lung cancer is not treated quickly, it is at risk of causing death. This research aimed to combine Synthetic Minority Over-sampling (Smote) and Random Forest methods for lung cancer classification. The method used was a combination of Smote and Random Forest. Smote was used to balance the data, while Random Forest was used to classify lung cancer data. The results showed that the combination of Smote and Random Forest methods obtained an accuracy of 94.1%, sensitivity of 94.5, and specificity of 93.7%. Meanwhile, without Smote, the accuracy is 89.1%, sensitivity is 55%, and specificity is 94.5%. The use of Smote can improve the performance of the Random Forest classification method based on accuracy and sensitivity. There was an increase of 5% in accuracy and a 39% increase in sensitivity.


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How to Cite
C. Michael Lauw, H. Hairani, I. Saifuddin, J. Ximenes Guterres, M. Maariful Huda, and M. Mayadi, “Combination of Smote and Random Forest Methods for Lung Cancer Classification”, International Journal of Engineering and Computer Science Applications (IJECSA), vol. 2, no. 2, pp. 63 - 70, Sep. 2023.

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