用模糊软集合理论评估乳腺癌风险，结合多项生理指标实现无创初筛。
Fuzzy Soft Set Theory based Expert System for the Risk Assessment in Breast Cancer Patients
- 基于模糊推理与软集合理论，融合五项生理指标进行风险建模。
- 在UCI数据集上验证，可有效识别高风险患者以指导后续诊断。
- 适合临床医生用于早期筛查，尤其适用于资源有限的医疗场景。
乳腺癌仍是全球女性主要致死原因之一，早期诊断对治疗和生存率至关重要。然而，由于疾病复杂性和患者风险因素差异，及时检测仍具挑战。本文提出一种基于模糊软集合理论的专家系统，利用可测量的临床与生理参数评估乳腺癌风险。输入变量包括体重指数、胰岛素水平、瘦素水平、脂联素水平及年龄，通过一组模糊推理规则与软集合理算，估算患者风险。这些参数均来自常规血液检查，实现无创且易获取的初步评估。模型开发与验证使用了来自UCI机器学习库的数据集。该系统旨在帮助医疗人员识别高风险患者，判断是否需要进一步诊断（如活检）。
原文摘要 · Abstract (English)
Breast cancer remains one of the leading causes of mortality among women worldwide, with early diagnosis being critical for effective treatment and improved survival rates. However, timely detection continues to be a challenge due to the complex nature of the disease and variability in patient risk factors. This study presents a fuzzy soft set theory-based expert system designed to assess the risk of breast cancer in patients using measurable clinical and physiological parameters. The proposed system integrates Body Mass Index, Insulin Level, Leptin Level, Adiponectin Level, and age as input variables to estimate breast cancer risk through a set of fuzzy inference rules and soft set computations. These parameters can be obtained from routine blood analyses, enabling a non-invasive and accessible method for preliminary assessment. The dataset used for model development and validation was obtained from the UCI Machine Learning Repository. The proposed expert system aims to support healthcare professionals in identifying high-risk patients and determining the necessity of further diagnostic procedures such as biopsies.
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