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Optimized Ensemble Learning with SMOTE for Gastric Cancer Classification

Students & Supervisors

Student Authors
Sunipun Seemanto
Bachelor of Science in Computer Science & Engineering, FST
Mahmudul Haque Shakir
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Md. Saef Ullah Miah
Associate Professor, Faculty, FST

Abstract

Gastric cancer (GC) is a common and mortal cancer worldwide, and the classification of its subtypes can be used as independent factors in determining the prognosis and therapeutic strategies. However, due to overlapping morphological characteristics as well as class imbalance in clinical datasets, it is difficult to distinguish the histological subtypes accurately. The objective of this study is to develop an optimal ensemble learning framework which balances the data through the SMOTE and applies a hybrid soft voting ensemble which is formed with Random Forest, XGBoost and Logistic Regression classifiers. This study compare the results between the baseline and ensemble models. The findings indicate a noteworthy enhancement produced by the use of class rebalancing for minority classes. The ensemble model also performed better than all other single models as indicated by an accuracy of 90%. These results demonstrate that ensemble learning integrated with class balancing approach can achieve an accurate and equitable classification of gastric cancer subtypes, contributing to guiding precise and personalized clinical decisions.

Keywords

Gastric Cancer Machine Learning Deep Learning Ensemble Learning Balancing

Publication Details

  • Type of Publication:
  • Conference Name: 4th IEEE IEEE Conference on Biomedical Engineering, Computer and Information Technology for Health 2025 ( IEEE BECITHCON 2025)
  • Date of Conference: 29/11/2025 - 29/11/2025
  • Venue: Eastern University, Dhaka, Bangladesh