Multiple attribute frequent mining-based for dengue outbreak

Authors:
Zalizah Awang Long;Azuraliza Abu Bakar;Abdul Razak Hamdan;Mazrura Sahani
Affiliations:
Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Faculty of Allied Health Science, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
Venue:
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Year:
2010

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Abstract

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.