International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Prediction Model for Gastric Cancer via Class Balancing Techniques

  • Danish, Jamil (Department of Information Technology, Malaysia University of Science and Technology) ;
  • Sellappan, Palaniappan (Department of Information Technology, Malaysia University of Science and Technology) ;
  • Sanjoy Kumar, Debnath (Chitkara University Institute of Engineering and Technology, Chitkara University) ;
  • Muhammad, Naseem (Department of Software Engineering, Syed University of Engineering and Technology) ;
  • Susama, Bagchi (Chitkara University Institute of Engineering and Technology, Chitkara University) ;
  • Asiah, Lokman (Department of Information Technology, Malaysia University of Science and Technology)
  • Received : 2023.01.05
  • Published : 2023.01.30
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Abstract

Many researchers are trying hard to minimize the incidence of cancers, mainly Gastric Cancer (GC). For GC, the five-year survival rate is generally 5-25%, but for Early Gastric Cancer (EGC), it is almost 90%. Predicting the onset of stomach cancer based on risk factors will allow for an early diagnosis and more effective treatment. Although there are several models for predicting stomach cancer, most of these models are based on unbalanced datasets, which favours the majority class. However, it is imperative to correctly identify cancer patients who are in the minority class. This research aims to apply three class-balancing approaches to the NHS dataset before developing supervised learning strategies: Oversampling (Synthetic Minority Oversampling Technique or SMOTE), Undersampling (SpreadSubsample), and Hybrid System (SMOTE + SpreadSubsample). This study uses Naive Bayes, Bayesian Network, Random Forest, and Decision Tree (C4.5) methods. We measured these classifiers' efficacy using their Receiver Operating Characteristics (ROC) curves, sensitivity, and specificity. The validation data was used to test several ways of balancing the classifiers. The final prediction model was built on the one that did the best overall.

Keywords

Acknowledgement

We would like to express our sincere appreciation to the NHS Liverpool hospital in England for making the dataset available to us to enable us to conduct this study. We would like to extend our sincere gratitude to Dr. Shakil Ahmed, a consultant surgeon at the Royal Liverpool hospital.

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