Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study

Rhamadina Fitrah Umami; Riyanarto Sarno

doi:10.1109/iSemantic50169.2020.9234295

Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study

Rhamadina Fitrah Umami
, Riyanarto Sarno

Institut Teknologi Sepuluh Nopember

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

7 Citations (Scopus)

Abstract

Breast cancer is a disease that causes excessive fear in women around the world. The number of high death rates by breast cancer can be reduced by early detection. This can make breast cancer a disease that is easy to cure. A collection of datasets about breast cancer is used in the process of early detection. Early detection is carried out to analyze the state of the early stages of breast cancer patients. This research paper proposes machine learning methods, namely Generalized Linear Model, Logistic Regression, and Gradient Boosted Decision Tree to enhance the classification performance of Wisconsin Diagnostic Breast Cancer Data. The diagnosis results in two classes of cancer decisions which are malignant and benign by looking at evaluating the accuracy of the data classification test. The result shows that the Generalized Linear Model achieves the accuracy of 99.4%, which is higher than the accuracies of the previous studies for classifying the Wisconsin Diagnostic Breast Cancer dataset.

Original language	English
Title of host publication	Proceedings - 2020 International Seminar on Application for Technology of Information and Communication
Subtitle of host publication	IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	464-469
Number of pages	6
ISBN (Electronic)	9781728190686
DOIs	https://doi.org/10.1109/iSemantic50169.2020.9234295
Publication status	Published - 19 Sept 2020
Event	2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020 - Semarang, Indonesia Duration: 19 Sept 2020 → 20 Sept 2020

Publication series

Name	Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020

Conference

Conference	2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020
Country/Territory	Indonesia
City	Semarang
Period	19/09/20 → 20/09/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Breast Cancer
Generalized Linear Model
Gradient Boosted Decision Trees
Logistic Regression

Access to Document

10.1109/iSemantic50169.2020.9234295

Cite this

Fitrah Umami, R., & Sarno, R. (2020). Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study. In Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020 (pp. 464-469). Article 9234295 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iSemantic50169.2020.9234295

Fitrah Umami, Rhamadina ; Sarno, Riyanarto. / Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study. Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 464-469 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020).

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title = "Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study",

abstract = "Breast cancer is a disease that causes excessive fear in women around the world. The number of high death rates by breast cancer can be reduced by early detection. This can make breast cancer a disease that is easy to cure. A collection of datasets about breast cancer is used in the process of early detection. Early detection is carried out to analyze the state of the early stages of breast cancer patients. This research paper proposes machine learning methods, namely Generalized Linear Model, Logistic Regression, and Gradient Boosted Decision Tree to enhance the classification performance of Wisconsin Diagnostic Breast Cancer Data. The diagnosis results in two classes of cancer decisions which are malignant and benign by looking at evaluating the accuracy of the data classification test. The result shows that the Generalized Linear Model achieves the accuracy of 99.4\%, which is higher than the accuracies of the previous studies for classifying the Wisconsin Diagnostic Breast Cancer dataset.",

keywords = "Breast Cancer, Generalized Linear Model, Gradient Boosted Decision Trees, Logistic Regression",

author = "\{Fitrah Umami\}, Rhamadina and Riyanarto Sarno",

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year = "2020",

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doi = "10.1109/iSemantic50169.2020.9234295",

language = "English",

series = "Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020",

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Fitrah Umami, R & Sarno, R 2020, Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study. in Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020., 9234295, Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020, Institute of Electrical and Electronics Engineers Inc., pp. 464-469, 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020, Semarang, Indonesia, 19/09/20. https://doi.org/10.1109/iSemantic50169.2020.9234295

Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study. / Fitrah Umami, Rhamadina; Sarno, Riyanarto.
Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 464-469 9234295 (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020).

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

TY - GEN

T1 - Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study

AU - Fitrah Umami, Rhamadina

AU - Sarno, Riyanarto

PY - 2020/9/19

Y1 - 2020/9/19

N2 - Breast cancer is a disease that causes excessive fear in women around the world. The number of high death rates by breast cancer can be reduced by early detection. This can make breast cancer a disease that is easy to cure. A collection of datasets about breast cancer is used in the process of early detection. Early detection is carried out to analyze the state of the early stages of breast cancer patients. This research paper proposes machine learning methods, namely Generalized Linear Model, Logistic Regression, and Gradient Boosted Decision Tree to enhance the classification performance of Wisconsin Diagnostic Breast Cancer Data. The diagnosis results in two classes of cancer decisions which are malignant and benign by looking at evaluating the accuracy of the data classification test. The result shows that the Generalized Linear Model achieves the accuracy of 99.4%, which is higher than the accuracies of the previous studies for classifying the Wisconsin Diagnostic Breast Cancer dataset.

AB - Breast cancer is a disease that causes excessive fear in women around the world. The number of high death rates by breast cancer can be reduced by early detection. This can make breast cancer a disease that is easy to cure. A collection of datasets about breast cancer is used in the process of early detection. Early detection is carried out to analyze the state of the early stages of breast cancer patients. This research paper proposes machine learning methods, namely Generalized Linear Model, Logistic Regression, and Gradient Boosted Decision Tree to enhance the classification performance of Wisconsin Diagnostic Breast Cancer Data. The diagnosis results in two classes of cancer decisions which are malignant and benign by looking at evaluating the accuracy of the data classification test. The result shows that the Generalized Linear Model achieves the accuracy of 99.4%, which is higher than the accuracies of the previous studies for classifying the Wisconsin Diagnostic Breast Cancer dataset.

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KW - Generalized Linear Model

KW - Gradient Boosted Decision Trees

KW - Logistic Regression

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DO - 10.1109/iSemantic50169.2020.9234295

M3 - Conference contribution

AN - SCOPUS:85096851011

T3 - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020

SP - 464

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BT - Proceedings - 2020 International Seminar on Application for Technology of Information and Communication

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Seminar on Application for Technology of Information and Communication, iSemantic 2020

Y2 - 19 September 2020 through 20 September 2020

ER -

Fitrah Umami R, Sarno R. Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study. In Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 464-469. 9234295. (Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020). doi: 10.1109/iSemantic50169.2020.9234295

Analysis of classification algorithm for Wisconsin diagnosis breast cancer data study

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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