A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm
Breast cancer represents a significant public health concern in both Western countries and Asia. Accurate and early detection is critical to improving long-term patient survival. For physicians to understand the classification and decision rules and to evaluate their results, it is preferable to use...
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MDPI AG
2025-07-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/6/7/154 |
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| author | Hui-Ching Wu Ming-Hseng Tseng |
| author_facet | Hui-Ching Wu Ming-Hseng Tseng |
| author_sort | Hui-Ching Wu |
| collection | DOAJ |
| description | Breast cancer represents a significant public health concern in both Western countries and Asia. Accurate and early detection is critical to improving long-term patient survival. For physicians to understand the classification and decision rules and to evaluate their results, it is preferable to use white box approaches to develop prediction models. This paper proposes a novel classification technique for extracting malignant prediction rules from training datasets containing numerical and binary nominal attributes. The classification technique introduced in this study facilitates the discovery of breast cancer patterns by integrating a real-coded genetic algorithm, an adaptive directed mutation operator, and a two-level malignant-rule-mining process. The experimental results, compared with existing rule-based methods from previous studies, demonstrate that the proposed approach generates simple and interpretable decision rules and effectively identifies patterns that lead to accurate breast cancer classification. |
| format | Article |
| id | doaj-art-ecda25a823de4c26b34ed0b865844d82 |
| institution | DOAJ |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-ecda25a823de4c26b34ed0b865844d822025-08-20T02:45:34ZengMDPI AGEng2673-41172025-07-016715410.3390/eng6070154A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic AlgorithmHui-Ching Wu0Ming-Hseng Tseng1Department of Medical Sociology and Social Work, Chung Shan Medical University, Taichung 402, TaiwanDepartment of Medical Informatics, Chung Shan Medical University, Taichung 402, TaiwanBreast cancer represents a significant public health concern in both Western countries and Asia. Accurate and early detection is critical to improving long-term patient survival. For physicians to understand the classification and decision rules and to evaluate their results, it is preferable to use white box approaches to develop prediction models. This paper proposes a novel classification technique for extracting malignant prediction rules from training datasets containing numerical and binary nominal attributes. The classification technique introduced in this study facilitates the discovery of breast cancer patterns by integrating a real-coded genetic algorithm, an adaptive directed mutation operator, and a two-level malignant-rule-mining process. The experimental results, compared with existing rule-based methods from previous studies, demonstrate that the proposed approach generates simple and interpretable decision rules and effectively identifies patterns that lead to accurate breast cancer classification.https://www.mdpi.com/2673-4117/6/7/154real-coded genetic algorithmadaptive directed mutationtwo-level malignant rulesbreast cancer |
| spellingShingle | Hui-Ching Wu Ming-Hseng Tseng A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm Eng real-coded genetic algorithm adaptive directed mutation two-level malignant rules breast cancer |
| title | A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm |
| title_full | A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm |
| title_fullStr | A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm |
| title_full_unstemmed | A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm |
| title_short | A Two-Level Rule-Mining Approach to Classify Breast Cancer Patterns Using Adaptive Directed Mutation and Genetic Algorithm |
| title_sort | two level rule mining approach to classify breast cancer patterns using adaptive directed mutation and genetic algorithm |
| topic | real-coded genetic algorithm adaptive directed mutation two-level malignant rules breast cancer |
| url | https://www.mdpi.com/2673-4117/6/7/154 |
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