Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification

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

doi:10.1109/DASA51403.2020.9317036

Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification

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

Department of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2020 International Conference on Decision Aid Sciences and Application, DASA 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	623-627
Number of pages	5
ISBN (Electronic)	9781728196770
DOIs	https://doi.org/10.1109/DASA51403.2020.9317036
Publication status	Published - 8 Nov 2020
Event	2020 International Conference on Decision Aid Sciences and Application, DASA 2020 - Virtual, Sakheer, Bahrain Duration: 7 Nov 2020 → 9 Nov 2020

Publication series

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

Conference

Conference	2020 International Conference on Decision Aid Sciences and Application, DASA 2020
Country/Territory	Bahrain
City	Virtual, Sakheer
Period	7/11/20 → 9/11/20

Keywords

Classification
Decision Tree
Logistic Regression
Machine Learning
Pancreatic Cancer

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DASA51403.2020.9317036

Cite this

Setiawan, Q. S., Rustam, Z., Hartini, S., Wibowo, V. V. P., & Aurelia, J. E. (2020). Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification. In 2020 International Conference on Decision Aid Sciences and Application, DASA 2020 (pp. 623-627). [9317036] (2020 International Conference on Decision Aid Sciences and Application, DASA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASA51403.2020.9317036

Setiawan, Qisthina Syifa ; Rustam, Zuherman ; Hartini, Sri et al. / Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification. 2020 International Conference on Decision Aid Sciences and Application, DASA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 623-627 (2020 International Conference on Decision Aid Sciences and Application, DASA 2020).

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title = "Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification",

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.",

keywords = "Classification, Decision Tree, Logistic Regression, Machine Learning, Pancreatic Cancer",

author = "Setiawan, {Qisthina Syifa} and Zuherman Rustam and Sri Hartini and Wibowo, {Velery Virgina Putri} and Aurelia, {Jane Eva}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 International Conference on Decision Aid Sciences and Application, DASA 2020 ; Conference date: 07-11-2020 Through 09-11-2020",

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doi = "10.1109/DASA51403.2020.9317036",

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Setiawan, QS, Rustam, Z, Hartini, S, Wibowo, VVP & Aurelia, JE 2020, Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification. in 2020 International Conference on Decision Aid Sciences and Application, DASA 2020., 9317036, 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 623-627, 2020 International Conference on Decision Aid Sciences and Application, DASA 2020, Virtual, Sakheer, Bahrain, 7/11/20. https://doi.org/10.1109/DASA51403.2020.9317036

Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification. / Setiawan, Qisthina Syifa; Rustam, Zuherman; Hartini, Sri et al.

2020 International Conference on Decision Aid Sciences and Application, DASA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 623-627 9317036 (2020 International Conference on Decision Aid Sciences and Application, DASA 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Setiawan, Qisthina Syifa

AU - Rustam, Zuherman

AU - Hartini, Sri

AU - Wibowo, Velery Virgina Putri

AU - Aurelia, Jane Eva

PY - 2020/11/8

Y1 - 2020/11/8

N2 - 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.

AB - 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.

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ER -

Setiawan QS, Rustam Z, Hartini S, Wibowo VVP, Aurelia JE. Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification. In 2020 International Conference on Decision Aid Sciences and Application, DASA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 623-627. 9317036. (2020 International Conference on Decision Aid Sciences and Application, DASA 2020). doi: 10.1109/DASA51403.2020.9317036

Comparing Decision Tree and Logistic Regression for Pancreatic Cancer Classification

Abstract

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Keywords

UN SDGs

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