Multiple Attribute Frequent Mining-Based for Dengue Outbreak

被引：0

作者：

Long, Zalizah Awang ^{[1
]}

Abu Bakar, Azuraliza ^{[1
]}

Hamdan, Abdul Razak ^{[1
]}

Sahani, Mazrura

机构：

[1] Univ Kebangsaan Malaysia, Ctr Artificial Intelligence Technol, Fac Informat Sci & Technol, Bangi 43600, Selangor, Malaysia

来源：

ADVANCED DATA MINING AND APPLICATIONS, ADMA 2010, PT I | 2010年 / 6440卷

关键词：

Frequent mining; outbreak; dengue; SURVEILLANCE; FEVER;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dengue fever (DF) and dengue hemorrhagic fever (DHF) are vector borne disease which is notifiable diseases in Malaysia since 1974. Early notification is essential for control measures as delayed notification will lead to further occurrences of outbreak cases. In this study we identify the number of attributes to be used in determining outbreaks rather than using only case counts. The experiment is conducted using multiple attribute value based on Apriori concept. The outcomes are promising when we can identify more than one attributes showing similar graph in vector-borne diseases outbreaks. Our methods also outperform in term of detection rate, false positive rate and overall performance. We prove through our experiment that more than one attributes can be used to better detect outbreaks.

引用

页码：489 / 496

页数：8

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