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

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.