中国医疗设备2016，Vol.31Issue(4)：30-35,6.DOI:10.3969/j.issn.1674-1633.2016.04.006
基于机器学习算法的前列腺癌诊断模型研究
Diagnostic Model Research of Prostate Cancer Based on Machine Learning Algorithm
摘要
Abstract
Objective To establish diagnostic prediction models based on three machine learning algorithms and compare the value of the three models in the diagnosis of prostate cancer (PC).Methods The research selected the clinical data of 956 patients (including 463 cases of prostate cancer and 493 cases of benign prostatic hyperplasia) with prostate biopsy in the General Hospital of PLA during 2008~2014. Predictors were screened by Logistic regression which included age, free prostate-speciifc antigen (fPSA), the percentage of free prostate-speciifc antigen (free PSA/total PSA), prostate volume, and PSA density (PSAD). The paper further compared the diagnostic accuracy of three models in the prediction of prostate cancer by using BP neural network, Logistic regression (LR), and random forest algorithm based on machine learning.ResultsThe diagnostic capability of Logistic regression, BP neural networks, and random forest model for prostate cancer was higher than any a single indicator. Retrospectively, the sensitivity of the three models were 77.5%, 77.4%, and 76.2% ; the speciifcity was 74.8%, 76.8%, and 76.9%; the accuracy was 76%, 77%, and 77%. The area under the ROC curve (AUC) was 0.831 for LR model, 0.832 for BP neural networks model, and 0.833 for the random forest model respectively, which indicated that there were no statistically signiifcant difference existing in the three modes in terms of diagnostic effectiveness. Conclusion The above results veriifed the high diagnostic validity of these three models, which all could be incorporated into urologic decision making to assist clinicians carry out diagnosis and treatment so as to reduce the unnecessary biopsies.关键词
前列腺癌/前列腺增生/诊断模型/Logistic回归/BP神经网络/随机森林Key words
prostate cancer/benign prostate hyperplasia/diagnostic model/Logistic regression/BP neural networks/random forest分类
信息技术与安全科学引用本文复制引用
曹文哲,应俊,张亚慧,马海洋,陈广飞,周丹..基于机器学习算法的前列腺癌诊断模型研究[J].中国医疗设备,2016,31(4):30-35,6.基金项目
国家自然科学基金（61501518）。 （）
