摘 要
肿瘤标志物的精准检测在癌症早期诊断和个性化治疗中具有重要意义，然而传统检测方法存在灵敏度不足、特异性较低及成本高昂等问题。本研究旨在利用机器学习技术提升肿瘤标志物检测的准确性和效率，为临床应用提供新思路。研究选取了多种常见肿瘤标志物作为分析对象，基于大规模公开数据集构建并优化了多个机器学习模型，包括支持向量机、随机森林以及深度神经网络等。通过特征选择算法提取关键生物标志物信息，并结合模型融合策略进一步提高预测性能。实验结果表明，所提出的深度学习模型在检测准确性上显著优于传统统计方法，其AUC值可达0.95以上，同时具备较强的泛化能力。
关键词：肿瘤标志物检测 机器学习 深度神经网络
Abstract
The accurate detection of tumor markers is of great significance in the early diagnosis and personalized treatment of cancer. However, the traditional detection methods have some problems, such as insufficient sensitivity, low specificity and high cost. This study aims to use machine learning technology to improve the accuracy and efficiency of tumor marker detection and provide new ideas for clinical application. The study selected many common tumor markers as analysis ob jects, and constructed and optimized several machine learning models based on large-scale public data sets, including support vector machines, random forests, and deep neural networks. Key biomarker information was extracted by the feature selection algorithm and further improved prediction performance. The experimental results show that the proposed deep learning model is significantly better than the traditional statistical methods in detection accuracy, with an AUC value above 0.95 and strong generalization ability.
Keyword:Tumor Marker Detection Machine Learning Deep Neural Network
目 录
1绪论 1
1.1肿瘤标志物检测的研究背景与意义 1
1.2机器学习在肿瘤标志物检测中的研究现状 1
1.3本文研究方法与技术路线 2
2数据处理与特征提取 2
2.1肿瘤标志物数据的获取与预处理 2
2.2特征选择与降维方法分析 3
2.3基于机器学习的特征优化策略 3
2.4数据质量对模型性能的影响评估 4
2.5数据集构建与实验设计 4
3模型构建与算法选择 4
3.1机器学习算法在肿瘤标志物检测中的应用 4
3.2常见分类模型的对比与评价 5
3.3深度学习模型的设计与改进 5
3.4集成学习方法在检测中的优势分析 6
3.5模型参数调优与性能提升 6
4实验验证与结果分析 7
4.1实验环境与数据集介绍 7
4.2不同模型的检测效果比较 7
4.3错误分析与改进方向探讨 8
4.4模型泛化能力与稳定性测试 8
4.5实际应用场景中的可行性评估 9
结论 9
参考文献 11
致谢 12
