Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Discovering the Clinical Knowledge about Breast Cancer Diagnosis Using Rule-Based Machine Learning Algorithms

Sun Apr 14 01:16:16 2024

(2022) Discovering the Clinical Knowledge about Breast Cancer Diagnosis Using Rule-Based Machine Learning Algorithms. Health Education and Health Promotion. pp. 89-97. ISSN 25885715 (ISSN)

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Aims: Breast cancer represents one of the most prevalent cancers and is also the main cause of cancer-related deaths in women globally. Thus, this study was aimed to construct and compare the performance of several rule-based machine learning algorithms in predicting breast cancer. Instrument & Methods: The data were collected from the Breast Cancer Registry database in the Ayatollah Taleghani Hospital, Abadan, Iran, from December 2017 to January 2021 and had information from 949 non-breast cancer and 554 breast cancer cases. Then the mean values and K-nearest neighborhood algorithm were used for replacing the lost quantitative and qualitative data fields, respectively. In the next step, the Chi-square test and binary logistic regression were used for feature selection. Finally, the best rule-based machine learning algorithm was obtained based on comparing different evaluation criteria. The Rapid Miner Studio 7.1.1 and Weka 3.9 software were utilized. Findings: As a result of feature selection the nine variables were considered as the most important variables for data mining. Generally, the results of comparing rule-based machine learning demonstrated that the J-48 algorithm with an accuracy of 0.991, F-measure of 0.987, and also AUC of 0.9997 had a better performance than others. Conclusion: It’s found that J-48 facilitates a reasonable level of accuracy for correct BC risk prediction. We believe it would be beneficial for designing intelligent decision support systems for the early detection of high-risk patients that will be used to inform proper interventions by the clinicians. © 2022, Tarbiat Modares University. All rights reserved.

Item Type: Article
Creators:
CreatorsEmail
Nopour, R.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Keywords: Artificial Intelligence Breast Neoplasms Data Mining Decision Tree Machine Learning
Divisions:
Page Range: pp. 89-97
Journal or Publication Title: Health Education and Health Promotion
Journal Index: Scopus
Volume: 10
Number: 1
ISSN: 25885715 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/4145

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