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Volume 10, Issue 1 (2022)
Health Educ Health Promot 2022, 10(1): 89-97 |
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History
Received: 2021/09/14 | Accepted: 2021/11/28 | Published: 2022/04/10
Received: 2021/09/14 | Accepted: 2021/11/28 | Published: 2022/04/10
How to cite this article
Nopour R, Kazemi-Arpanahi H, Shanbehzadeh M. Discovering the Clinical Knowledge about Breast Cancer Diagnosis Using Rule-Based Machine Learning Algorithms. Health Educ Health Promot 2022; 10 (1) :89-97
URL: http://hehp.daneshafarand.org/article-4-55598-en.html
URL: http://hehp.daneshafarand.org/article-4-55598-en.html
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1- Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
2- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
3- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
2- Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran
3- Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
Abstract (2868 Views)
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.
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.
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