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Research on Logistic Regression Algorithm of Breast Cancer Diagnose Data by Machine Learning

机译：机器学习对乳腺癌诊断数据的Logistic回归算法研究

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If machine learning can automatically identify cancer cells, it will provide considerable benefits to the medical system. The process of automation is likely to improve the efficiency of the detection process, and it may also provide higher detection accuracy by removing the internal subjective human factors in the process. Starting from the measurement data of biopsy cells in women with abnormal breast masses, logistic regression algorithm is applied to study the efficiency of machine learning for cancer detection. In this paper, the LogisticRegression algorithm of Sklearn machine learning library is used to classify the data sets of breast cancer (diagnosis). The classification results show that when the two features of maximum texture and maximum perimeter are selected, the classification accuracy is 96.5%, which is improved compared with the previous methods.
机译：如果机器学习可以自动识别癌细胞，它将为医疗系统带来可观的收益。自动化过程可能会提高检测过程的效率，并且还可以通过消除过程中的内部主观人为因素来提供更高的检测精度。从乳腺异常妇女的活检细胞的测量数据开始，采用逻辑回归算法研究机器学习对癌症检测的效率。本文使用Sklearn机器学习库的Logistic回归算法对乳腺癌的数据集进行分类（诊断）。分类结果表明，当选择最大纹理和最大的两个特征周长，分类精度是96.5 \％，与以前的方法相比，其改善。

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