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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.12
- 수록면
- 530 - 543 (14page)
- DOI
- 10.4283/JMAG.2023.28.4.530
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초록· 키워드
오류제보하기
Breast cancer is a significant global health concern, stressing the urgent need for early detection. Early diagnosis improves access to varied treatments and significantly enhances patient outcomes. This study explores breast cancer detection over two days, aiming to create a precise and efficient machine learning model. The research uses a diverse dataset, combining clinical, genetic, and imaging data, including magnetic resonance imaging (MRI), X-ray, and electromagnetic data. Rigorous data preprocessing, including variable normalization and feature identification, enhances dataset quality. Predictive models use statistical techniques like logistic regression, decision trees, and random forest. Key metrics, such as accuracy, precision, recall, and area under the curve (AUC), assess model efficacy. Results reveal high accuracy and AUC scores, indicating potential for precise breast cancer detection. The study enhances our understanding of breast cancer dynamics, showcasing the effectiveness of machine learning for accurate and efficient early diagnosis. The research underscores diverse datasets and careful statistical modeling as crucial for predictive breast cancer capabilities.
목차
Abstract
1. Introduction
2. Related Work
3. Research Framework
4. Conclusion and Future Work
References
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