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Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015

机译：数据挖掘技术在2009年至2015年从乌鲁木齐艾乌鲁木齐艾滋病毒/艾滋病高风险群体监督报告数据的应用

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摘要

Objective. Urumqi is one of the key areas of HIV/AIDS infection in Xinjiang and in China. The AIDS epidemic is spreading from high-risk groups to the general population, and the situation is still very serious. The goal of this study was to use four data mining algorithms to establish the identification model of HIV infection and compare their predictive performance. Method. The data from the sentinel monitoring data of the three groups of high-risk groups (injecting drug users (IDU), men who have sex with men (MSM), and female sex workers (FSW)) in Urumqi from 2009 to 2015 included demographic characteristics, sex behavior, and serological detection results. Then we used age, marital status, education level, and other variables as input variables and whether to infect HIV as output variables to establish four prediction models for the three datasets. We also used confusion matrix, accuracy, sensitivity, specificity, precision, recall, and the area under the receiver operating characteristic (ROC) curve (AUC) to evaluate classification performance and analyzed the importance of predictive variables. Results. The final experimental results show that random forests algorithm obtains the best results, the diagnostic accuracy for random forests on MSM dataset is 94.4821%, 97.5136% on FSW dataset, and 94.6375% on IDU dataset. The k-nearest neighbors algorithm came out second, with 91.5258% diagnostic accuracy on MSM dataset, 96.3083% diagnostic accuracy on FSW dataset, and 90.8287% diagnostic accuracy on IDU dataset, followed by support vector machine (94.0182%, 98.0369%, and 91.3571%). The decision tree algorithm was the poorest among the four algorithms, with 79.1761% diagnostic accuracy on MSM dataset, 87.0283% diagnostic accuracy on FSW dataset, and 74.3879% accuracy on IDU. Conclusions. Data mining technology, as a new method of assisting disease screening and diagnosis, can help medical personnel to screen and diagnose AIDS rapidly from a large number of informatio
机译：客观的。乌鲁木齐是新疆和中国艾滋病毒/艾滋病感染的关键领域之一。艾滋病疫情正在从高风险群体传播到一般人群，情况仍然非常严重。本研究的目标是使用四种数据挖掘算法来建立HIV感染的鉴定模型，并比较他们的预测性能。方法。来自2009年至2015年乌鲁木齐的三组高风险群体（注射吸毒者（IDU），与男性（MSM）和女性性工作者（FSW）发生性发生性关系的男性的数据，包括人口统计特征，性行为和血清学检测结果。然后我们使用年龄，婚姻状况，教育水平和其他变量作为输入变量以及是否感染艾滋病毒作为输出变量，为三个数据集建立四个预测模型。我们还使用了混淆矩阵，准确性，灵敏度，特异性，精度，召回和接收器操作特征（ROC）曲线（AUC）下的区域，以评估分类性能并分析预测变量的重要性。结果。最终的实验结果表明，随机森林算法获得了最佳结果，MSM数据集随机林的诊断准确性为94.4821％，97.5136％，在FSW数据集中为94.6375％。 K-CORMATE邻居算法阐述了MSM数据集的91.5258％，FSW数据集的诊断准确度为91.5258％，在IDU数据集中的诊断准确度为96.3083％，其次是支持向量机（94.0182％，98.0369％和91.3571 ％）。决策树算法是四种算法中最糟糕的算法，在MSM数据集中具有79.1761％的诊断准确性，FSW数据集的诊断准确性为87.0283％，对IDU的准确性为74.3879％。结论。数据挖掘技术，作为辅助疾病筛查和诊断的新方法，可以帮助医务人员从大量信息中迅速筛选和诊断艾滋病

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  • 来源
    《Complexity》 |2018年第17期|共17页
  • 作者

    Dandan Tang; Man Zhang; Jiabo Xu; Xueliang Zhang; Fang Yang; Huling Li; Li Feng; Kai Wang; Yujian Zheng;

  • 作者单位

    College of Public Health Xinjiang Medical University Urumqi 830011 China;

    Department of Information Engineering Xinjiang Institute of Engineering Urumqi 830000 China;

    College of Medical Engineering and Technology Xinjiang Medical University Urumqi 830011 China;

    Department of Medical Engineering The Affiliated Tumor Hospital Xinjiang Medical University Urumqi 830011 China;

    Department of AIDS/STD Control and Prevention Urumqi Center for Disease Control and Prevention Urumqi Xinjiang 830026 China;

    College of Public Health Xinjiang Medical University Urumqi 830011 China;

    College of Public Health Xinjiang Medical University Urumqi 830011 China;

    Department of Medical Engineering The Affiliated Tumor Hospital Xinjiang Medical University Urumqi 830011 China;

    College of Public Health Xinjiang Medical University Urumqi 830011 China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大系统理论;
  • 关键词

    Application; Data Mining Technology; Surveillance Report Data;

    机译：应用;数据挖掘技术;监督报告数据;

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