数据挖掘与机器学习在乳腺癌预后预测中的应用综述

A Review of Data Mining and Machine Learning Applications in Breast Cancer Prognostic Prediction

摘要: 乳腺癌作为全球女性最常见的恶性肿瘤之一，其高发病率和死亡率对公共卫生构成严峻挑战。随着医疗信息化的发展和大数据技术的进步，基于数据挖掘和机器学习的乳腺癌预后预测模型已成为精准医学研究的热点。本文系统综述了数据挖掘与机器学习技术在乳腺癌生存率预测领域的研究进展。首先阐述了乳腺癌预测模型构建中的关键数据预处理环节，包括数据清洗、缺失值填补和特征选择的主要方法及其优缺点。其次，系统梳理了应用于乳腺癌生存预测的各类机器学习模型，从传统的贝叶斯网络、逻辑回归、支持向量机到先进的深度学习技术，并分析了不同模型的应用场景与性能特点。进而，深入探讨了当前研究中面临的普遍挑战，如数据缺失、类别不平衡和模型可解释性等问题，并总结了SMOTE过采样、混合模型填补、生成对抗网络数据增强等主流应对策略。最后，展望了该领域的未来发展方向，包括多模态数据融合、全自动化模型构建以及基于因果推断的特征选择等前沿技术。本综述旨在为乳腺癌的精准医疗和临床决策支持提供系统性的理论参考。

Abstract: Breast cancer, as one of the most prevalent malignancies among women globally, poses a significant public health challenge due to its high incidence and mortality rates. With the advancement of medical informatization and big data technologies, prognostic prediction models for breast cancer based on data mining and machine learning have emerged as a focal point in precision medicine research. This paper systematically reviews the research progress of data mining and machine learning techniques in the field of breast cancer survival prediction. It begins by elucidating the critical data preprocessing steps in constructing breast cancer prediction models, including data cleaning, missing value imputation, and feature selection, along with their primary methods, advantages, and limitations. Subsequently, it systematically categorizes various machine learning models applied to breast cancer survival prediction, ranging from traditional models such as Bayesian networks, logistic regression, and support vector machines to advanced deep learning techniques, and analyzes their application scenarios and performance characteristics. Furthermore, it critically examines the prevalent challenges in current research, such as missing data, class imbalance, and model interpretability, and summarizes mainstream coping strategies including SMOTE oversampling, hybrid model imputation, and Generative Adversarial Network-based data augmentation. Finally, it discusses future directions in this field, encompassing cutting-edge technologies such as multi-modal data fusion, fully automated model construction, and causal inference-based feature selection. This review aims to provide a systematic theoretical reference for precision medicine and clinical decision support in breast cancer.