A Comparative Machine Learning Algorithm to Predict the Bone Metastasis Cervical Cancer with Imbalance Data Problem

  • Kasama Dokduang
  • Sirapat Chiewchanwattana
  • Khamron Sunat
  • Vorachai Tangvoraphonkchai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)


This paper attempted to develop and validate a tool to predict the immediate results of radiation on bone metastasis in cervical cancer cases. Cases of bone metastasis in cervical cancer are based on radiation treatment data, which is imbalanced. This imbalanced data is a challenge among the researchers in data mining, called class imbalance learning (CIL) and has lead to difficulties in machine learning and a reduction in the classifier performance. In this paper, we compared several algorithms to deal with the data imbalance classification problem using the synthetic minority over-sampling technique (SMOTE) used to drive classification models: Ant-Miner, RIPPER, Ridor, PART, ADTree, C4.5, ELM and Weighted ELM using Accuracy, G-mean and F-measure to evaluate performance. The results of this paper show that the RIPPER algorithm outperformed the other algorithms in Accuracy and F-measure, but weighted ELM outperformed other algorithms by G-mean. This may be useful when evaluating clinical assessments.


cervical cancer classification algorithm radiotherapy imbalance data machine learning metastasis 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kasama Dokduang
    • 1
  • Sirapat Chiewchanwattana
    • 1
  • Khamron Sunat
    • 1
  • Vorachai Tangvoraphonkchai
    • 2
  1. 1.Department of Computer Science, Faculty of ScienceKhon Kaen UniversityKhon KaenThailand
  2. 2.Department of Radiology, Faculty of MedicineKhon Kaen UniversityKhon KaenThailand

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