机器学习算法在乳腺癌预测中的应用

Application of Machine Learning Algorithms in Breast Cancer Prediction

摘要: 乳腺癌是世界上女性最常见的恶性肿瘤，治愈乳腺癌的关键在于早期的诊断和治疗。及时诊断肿瘤对临床治疗具有重要意义，因此，找到一种能够准确识别肿瘤类型并尽早进行治疗的算法变得尤为关键。本文介绍了在威斯康星州诊断乳腺癌数据集上使用了lasso算法进行特征筛选，然后基于这些特征训练了随机森林分类器来预测乳腺癌的良性或恶性。结果显示，预测模型的准确率为95.32%，召回率为92.06%，F1分数为93.55%，通过这些指标的综合评估，证明这种方法可以有效地进行乳腺癌良恶性的预测，具有潜在的应用价值。总的来说，文中提供了一种有力的方法，可以对癌症数据进行预测，并优化分类器的性能。这种方法可以帮助医生更好地诊断乳腺癌，促进更好的治疗和预防，对乳腺癌的研究具有重要的意义。

Abstract: Breast cancer is the most common malignant tumor in women worldwide, and early diagnosis and treatment are key to curing breast cancer. Timely detection of tumors is of great significance for clinical treatment, so finding an algorithm that can accurately identify tumor types and start treatment early is crucial. This article introduces the use of the lasso algorithm for feature selection on a breast cancer diagnostic dataset in Wisconsin. Based on these features, a random forest classifier was trained to predict the benign or malignant nature of breast cancer. The results showed an accuracy of 95.32%, a recall rate of 92.06%, and an F1 score of 93.55% for the predictive model. Through a comprehensive evaluation of these metrics, it is proven that this method can effectively predict the benign or malignant nature of breast cancer and has potential practical value. In summary, the article provides a powerful method for predicting cancer data and optimizing the performance of classifiers. This approach can help doctors better diagnose breast cancer, promote better treatment and prevention, and has significant implications for breast cancer research.