Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015
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 identifica...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/9193248 |
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| _version_ | 1850233714071568384 |
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| author | Dandan Tang Man Zhang Jiabo Xu Xueliang Zhang Fang Yang Huling Li Li Feng Kai Wang Yujian Zheng |
| author_facet | Dandan Tang Man Zhang Jiabo Xu Xueliang Zhang Fang Yang Huling Li Li Feng Kai Wang Yujian Zheng |
| author_sort | Dandan Tang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-06f98b9cbe3144caa951fc71610bd47c |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-06f98b9cbe3144caa951fc71610bd47c2025-08-20T02:02:51ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/91932489193248Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015Dandan Tang0Man Zhang1Jiabo Xu2Xueliang Zhang3Fang Yang4Huling Li5Li Feng6Kai Wang7Yujian Zheng8College of Public Health, Xinjiang Medical University, Urumqi 830011, ChinaDepartment of Information Engineering, Xinjiang Institute of Engineering, Urumqi, 830000, ChinaCollege of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, ChinaDepartment of Medical Engineering, The Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi 830011, ChinaDepartment of AIDS/STD Control and Prevention, Urumqi Center for Disease Control and Prevention, Urumqi, Xinjiang 830026, ChinaCollege of Public Health, Xinjiang Medical University, Urumqi 830011, ChinaCollege of Public Health, Xinjiang Medical University, Urumqi 830011, ChinaDepartment of Medical Engineering, The Affiliated Tumor Hospital, Xinjiang Medical University, Urumqi 830011, ChinaCollege of Public Health, Xinjiang Medical University, Urumqi 830011, ChinaObjective. 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.http://dx.doi.org/10.1155/2018/9193248 |
| spellingShingle | Dandan Tang Man Zhang Jiabo Xu Xueliang Zhang Fang Yang Huling Li Li Feng Kai Wang Yujian Zheng Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 Complexity |
| title | Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 |
| title_full | Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 |
| title_fullStr | Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 |
| title_full_unstemmed | Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 |
| title_short | Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 |
| title_sort | application of data mining technology on surveillance report data of hiv aids high risk group in urumqi from 2009 to 2015 |
| url | http://dx.doi.org/10.1155/2018/9193248 |
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