Application of Data Mining Technology on Surveillance Report Data of HIVAIDS High-Risk Group in Urumqi from 2009 to 2015
المؤلفون المشاركون
Zhang, Xueliang
Li, Huling
Tang, Dandan
Zhang, Man
Xu, Jiabo
Yang, Fang
Feng, Li
Zheng, Yujian
Wang, Kai
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-12-10
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
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 information.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Tang, Dandan& Zhang, Man& Xu, Jiabo& Zhang, Xueliang& Yang, Fang& Li, Huling…[et al.]. 2018. Application of Data Mining Technology on Surveillance Report Data of HIVAIDS High-Risk Group in Urumqi from 2009 to 2015. Complexity،Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1136570
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Tang, Dandan…[et al.]. Application of Data Mining Technology on Surveillance Report Data of HIVAIDS High-Risk Group in Urumqi from 2009 to 2015. Complexity No. 2018 (2018), pp.1-17.
https://search.emarefa.net/detail/BIM-1136570
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Tang, Dandan& Zhang, Man& Xu, Jiabo& Zhang, Xueliang& Yang, Fang& Li, Huling…[et al.]. Application of Data Mining Technology on Surveillance Report Data of HIVAIDS High-Risk Group in Urumqi from 2009 to 2015. Complexity. 2018. Vol. 2018, no. 2018, pp.1-17.
https://search.emarefa.net/detail/BIM-1136570
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1136570
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر