Predictive models for dengue outbreak using multiple rulebase classifiers

Bakar, Azuraliza Abu; Kefli, Zuriyah; Abdullah, Salwani; Sahani, Maneesh

doi:10.1109/iceei.2011.6021830

Cited by 41 publications

(30 citation statements)

References 11 publications

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“…Husin and Salim (2008) employ artificial neural networks with back propagation algorithm and non-linear regression models to predict the next dengue outbreak for 2 dengue datasets and rainfall dataset with variation in terms of time and location. Bakar et al (2011) proposes a predictive model based on multiple rule-based classifiers to detect dengue outbreak using a dataset containing 8505 dengue patient records with 134 attributes. Moreover, Long et al (2010) present an interesting study of pattern mining in outbreak detection using Apriori model which shows promising results in this domain.…”

Section: Related Work Dengue Outbreak Detectionmentioning

confidence: 99%

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Husam¹,

Abuhamad²,

Bakar³

et al. 2017

JSM

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Section: Related Work Dengue Outbreak Detectionmentioning

confidence: 99%

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Husam¹,

Abuhamad²,

Bakar³

et al. 2017

JSM

Self Cite

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“…The classifiers were investigated individually as well as combinatory in order to study their performance. On the basis of experimental results, it has been found that the accuracy of multiple classifiers showed better was better accuracy than single classifier [37]. Shweta Kharya discussed various data mining approaches that have been utilized for breast cancer diagnosis and prognosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining for diagnosis in Healthcare Sector-a review

2017

IJASRE

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“…The classifiers were investigated individually and also in combination in order to study their performance. On the basis of experimental results, it has been found that the accuracy of multiple classifiers was better than the accuracy of single classifier [72].…”

Section: Fei Et Al Proposed Particle Swarm Optimization -Supportmentioning

confidence: 99%

Techniques of Data Mining In Healthcare: A Review

Ahmad¹,

Qamar²,

Rizvi³

2015

IJCA

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Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is the best suited. Data mining have a great potential to enable healthcare systems to use data more efficiently and effectively. Hence, it improves care and reduces costs. This paper reviews various Data Mining techniques such as classification, clustering, association, regression in health domain. It also highlights applications, challenges and future work of Data Mining in healthcare.

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Predictive models for dengue outbreak using multiple rulebase classifiers

Cited by 41 publications

References 11 publications

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Data Mining for diagnosis in Healthcare Sector-a review

Techniques of Data Mining In Healthcare: A Review

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