Multiple attribute frequent mining-based for dengue outbreak

Zalizah Awang Long, Azuraliza Abu Bakar, Abdul Razak Hamdan, Mazrura Sahani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages489-496
Number of pages8
Volume6440 LNAI
EditionPART 1
DOIs
Publication statusPublished - 2010
Event6th International Conference on Advanced Data Mining and Applications, ADMA 2010 - Chongqing
Duration: 19 Nov 201021 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6440 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Conference on Advanced Data Mining and Applications, ADMA 2010
CityChongqing
Period19/11/1021/11/10

Fingerprint

Mining
Attribute
Malaysia
Experiments
False Positive
Experiment
Count
Term
Graph in graph theory

Keywords

  • dengue
  • Frequent mining
  • outbreak

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Long, Z. A., Abu Bakar, A., Hamdan, A. R., & Sahani, M. (2010). Multiple attribute frequent mining-based for dengue outbreak. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 ed., Vol. 6440 LNAI, pp. 489-496). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6440 LNAI, No. PART 1). https://doi.org/10.1007/978-3-642-17316-5_46

Multiple attribute frequent mining-based for dengue outbreak. / Long, Zalizah Awang; Abu Bakar, Azuraliza; Hamdan, Abdul Razak; Sahani, Mazrura.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6440 LNAI PART 1. ed. 2010. p. 489-496 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6440 LNAI, No. PART 1).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Long, ZA, Abu Bakar, A, Hamdan, AR & Sahani, M 2010, Multiple attribute frequent mining-based for dengue outbreak. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 edn, vol. 6440 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6440 LNAI, pp. 489-496, 6th International Conference on Advanced Data Mining and Applications, ADMA 2010, Chongqing, 19/11/10. https://doi.org/10.1007/978-3-642-17316-5_46
Long ZA, Abu Bakar A, Hamdan AR, Sahani M. Multiple attribute frequent mining-based for dengue outbreak. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 1 ed. Vol. 6440 LNAI. 2010. p. 489-496. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). https://doi.org/10.1007/978-3-642-17316-5_46
Long, Zalizah Awang ; Abu Bakar, Azuraliza ; Hamdan, Abdul Razak ; Sahani, Mazrura. / Multiple attribute frequent mining-based for dengue outbreak. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6440 LNAI PART 1. ed. 2010. pp. 489-496 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).
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