摘要
目的探索应用人工智能机器学习算法单纯基于肝、肾功等血液学检查来辅助筛查泌尿系统肿瘤。方法分别利用支持向量机和神经网络算法对3136例正常人员和泌尿系统恶性肿瘤患者肝肾功数据进行分析,找到肝肾功数据与泌尿系统恶性肿瘤的相关性。结果对于泌尿系统恶性肿瘤通过5次交叉验证的最优平均分类准确率达到了92.05%,支持向量机与神经网络算法结果基本一致。结论机器学习算法可以单纯通过肝、肾功等血液学检测分类正常人和泌尿系统恶性肿瘤患者,表明该方法有望成为一种泌尿系统肿瘤辅助筛查手段。
Objective To explore a new assist method in the diagnosis of urological carcinoma based on routine hematological examination by deep learning. Method The support vector machine （ SVM）and artificial neural network（ ANN）were applied to distinguish the urological carcinoma by analyzing the data of routine hemato logical examination from 3163 patients including normal persons and urological carcinoma cases. Results The average accuracy rate of deep learning method through 5 cross validation was 92. 05% for urological urological carcinoma cases. The accuracy was no significant differences between SVM and ANN. Conclusions There is probability to classify normal and urological carcinoma patients by using deep learning method on the routine hematological examination.
作者
王正
王金申
刘志
季凯
刘义庆
金讯波
WANG Zheng;WANG Jin - shen;LIU Zhi;JI Kai;LIU Yi - qing;JIN Xun - bo(Minimally Invasive Urology Center,Provincial Hospital Affiliated to Shandong University,Jinan,Shandong,250014,China;Department of GastrointestinalSurgery,Shandong Provincial Hospital Affiliated to Shandong University,Jinan,Shandong,250014,China;Department of clinical laboratory,ShandongProvincial Hospital Affiliated to Shandong University,Jinan,Shandong,250014,China;Shandong Helix Matrix Technology Co. , Ltd,Jinan,Shandong,250014,China)
出处
《泌尿外科杂志（电子版）》
2017年第4期9-14,共6页
Journal of Urology for Clinicians(Electronic Version)
基金
山东省重点研发计划(No.2017G006007)基金支持
关键词
血液学检测
机器学习
支持向量机
神经网络
泌尿系统肿瘤
疾病筛查
Hematological examination
Deep learning
Support Vector Machine
Artificial neural network
Urinary system carcinoma
Disease screening
