用于有效诊断和预测乳腺癌患者生存期的机器学习系统

Arturo Gago, Jean Marko Aguirre, Lenis Wong
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引用次数: 0

摘要

乳腺癌是全球健康面临的最重大挑战之一。有效的诊断和预后预测对于改善这种疾病的患者预后至关重要。由于机器学习(ML)在许多学科中都极大地改进了预测模型,因此本研究的目标是为医学专家开发一种 ML 系统,以准确预测乳腺癌患者的肿瘤诊断和生存期。在诊断和生存预测的训练中,使用了五种算法模型--决策树(DT)、随机森林(RF)、奈夫贝叶斯(NB)、支持向量机(SVM)和梯度提升--对威斯康星乳腺癌数据集的 569 条记录和乳腺癌基因表达谱数据集的 1,980 条记录进行了训练。结果表明,NB 模型在肿瘤诊断方面表现更佳,准确率达到 95.0%,而 RF 模型在患者存活率方面表现最佳,准确率达到 76.0%。对医学专家使用该系统的经验进行的调查显示,该系统在可靠性、性能、满意度、可用性和效率方面都获得了高分,这证实了 ML 系统具有改善乳腺癌患者预后的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning System for the Effective Diagnosis and Survival Prediction of Breast Cancer Patients
Breast cancer is one of the most significant global health challenges. Effective diagnosis and prognosis prediction are crucial for improving patient outcomes in the case of this disease. As machine learning (ML) has significantly improved prediction models in many disciplines, the goal of this study is to develop a ML system for medical specialists that can accurately predict tumor diagnosis and patient survival for breast cancer patients. For the training of diagnosis and survival prediction, five algorithmic models—decision tree (DT), random forest (RF), naive bayes (NB), support vector machines (SVMs), and gradient boosting—were trained with 569 records from the Breast Cancer Wisconsin dataset and 1,980 records from the Breast Cancer Gene Expression Profiles dataset. The results showed that the NB model exhibited better performance for tumor diagnosis, achieving an accuracy of 95.0%, while RF presented the best results for patient survival, with an accuracy of 76.0%. A survey of medical experts’ experience with the resulting system showed high scores in reliability, performance, satisfaction, usability, and efficiency, confirming that ML systems have the potential to improve breast cancer patient outcomes.
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