Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification

Qisthina Syifa Setiawan, Zuherman Rustam, Sri Hartini, Velery Virgina Putri Wibowo, Jane Eva Aurelia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The kind of disease which causes the development of abnormal cells in any part of the body and also leads to death is called cancer. Pancreatic cancer is a type of cancer which is marked when abnormal cells start to develop in the pancreas. Sometimes, the affected individuals do not show any signs or symptoms at an early stage. There are treatments which are chosen based on how wide it has spread with the aim of extending the lives of those affected. Therefore, classification algorithms of machine learning with the best accuracy are needed to assist the medical field in classifying individuals with pancreatic cancer. In this research, classification algorithms of Decision Tree and Logistic Regression were used. Furthermore, these two methods were compared to discover which has the best performance based on accuracy. The results showed that the Decision Tree and Logistic Regression yielded 100% and 92.68% respectively as their highest accuracy. Therefore, the Decision Tree is a better method based on accuracy for classifying pancreatic cancer.

Original languageEnglish
Title of host publication2020 International Conference on Decision Aid Sciences and Application, DASA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages623-627
Number of pages5
ISBN (Electronic)9781728196770
DOIs
Publication statusPublished - 8 Nov 2020
Event2020 International Conference on Decision Aid Sciences and Application, DASA 2020 - Virtual, Sakheer, Bahrain
Duration: 7 Nov 20209 Nov 2020

Publication series

Name2020 International Conference on Decision Aid Sciences and Application, DASA 2020

Conference

Conference2020 International Conference on Decision Aid Sciences and Application, DASA 2020
Country/TerritoryBahrain
CityVirtual, Sakheer
Period7/11/209/11/20

Keywords

  • Classification
  • Decision Tree
  • Logistic Regression
  • Machine Learning
  • Pancreatic Cancer

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