Gastrointestinal Cancer Classification by Symptomatology and Gene Expression Data Using Machine Learning

EasyChair Preprint 15681

Abstract

Cancer is a leading cause of mortality, with ten million deaths annually. This study focuses on classifying gastrointestinal (GI) cancers—esophageal, liver, colon, stomach, and pancreatic—using symptomatology and gene expression data. An advanced machine learning (ML) model combining SVM, RF, DT, and LR demonstrated superior accuracy compared to ten other ML algorithms. The findings highlight ML's potential to enhance diagnostics, offering precise and effective medical interventions.

Keyphrases: Cancer Classification, Colon cancer, Esophageal cancer, Liver cancer, Pancreatic cancer, gastrointestinal cancer, gene expression, machine learning, stomach cancer

@booklet{EasyChair:15681,
  author    = {Tamanna Islam and Bithy Khanam and Fahmida Sultana and Kingkar Prosad Ghosh and Md. Kawsar Ahmed and Apurba Kumar Barman},
  title     = {Gastrointestinal Cancer Classification by Symptomatology and Gene Expression Data Using Machine Learning},
  howpublished = {EasyChair Preprint 15681},
  year      = {EasyChair, 2025}}