DATA MINING ON DENGUE VIRUS DISEASE

摘要：

Dengue infection is an epidemic disease typically found in tropical region. Symptoms of the disease show rapid and violent for patients in a short time. The World Health Organization (WHO) classifies the dengue infection as Dengue Fever (DF) and Dengue Hemorrhagic Fever (DHF). Symptoms of DHF are divided into 4 types. The problem might be happen when an expert misdiagnoses dengue infection. For Example, an expert diagnosed a patient as non dengue or DF even if a patient was a DHF patient. That might be the cause of dead if patient did not receive treatment. Therefore, we selected data mining approach to solve this problem. We employed decision tree algorithm to learn from data set in order to create new knowledge. The first experimental result shows useful knowledge to classify dengue infection levels into 4 groups (DF, DHF I, DHF II, and DHF III). An average accuracy is 96.50 ％. The second experimental result shows the tree and a set of rules to classify dengue infection levels into 2 groups followed by our assumption. An accuracy is 96.00 ％. Furthermore, we compared our performance in term of false negative values to WHO and some researchers and found that our research outperforms those criteria, as well.

关键词： Data mining Decision tree Dengue virus disease

作者: Daranee Thitiprayoonwongse1 Prapat Suriyaphol Nuanwan Soonthornphisaj

作者单位: Department of Computer Science, Faculty of Science Kasetsart University, Bangkok, Thailand 2Bioinformatics and Data Management for Research Unit Office for Research and Development Siriraj Ho

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 1932-1941

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）