乳腺癌具备易于复发性和高死亡率等特点,已成为女性癌症患者死亡的重要原因.乳腺癌的早期诊断可增加癌症治愈的可能性,因此,提高早期诊断的准确性尤为重要.传统的早期诊断主要依靠人类经验,通过分析临床或检查数据来判断乳腺癌,无法保证足够的准确性.许多研究人员提出了各种机器学习方法,以提高预测的准确性和效率.但现有的算法计算复杂性很高,并且难以从多种算法中直接确定最适合的算法.本文尝试了10种流行的分类算法,比较了它们之间的差异,并应用了量子支持向量机来加速计算过程.数值实验显示支持向量机和人工神经网络的预测效果最佳,表明了多种分类算法混合比较的有效性.“,”Breast cancer is an important cause of the death of female cancer patients due to its easy recurrence and high mortality. Early diagnosis of breast cancer increases the probability of curing cancer. Therefore, it is particularly important to improve the accuracy of early diagnosis. The traditional early diagnosis mainly relies on human experience to judge breast cancer by analyzing clinical or examination data, and sufficient accuracy cannot be guaranteed. Many researchers have proposed various machine learning methods to improve prediction accuracy and efficiency. However, the current algorithms have high computational complexity, and it is difficult to directly determine the appropriate algorithm from a variety of algorithms. This paper experiments with ten popular classification algorithms, compares the differences between the algorithms, and applies quantum support vector machines to speed up the computation process. Numerical experiments show that support vector machines and artificial neural network achieve the best prediction results, verifying the effectiveness of hybrid comparison of multiple classification algorithms.