Multiple Attribute Frequent Mining-Based for Dengue Outbreak

Long, Zalizah Awang; Bakar, Azuraliza Abu; Hamdan, Abdul Razak; Sahani, Maneesh

doi:10.1007/978-3-642-17316-5_46

Cited by 7 publications

(12 citation statements)

References 8 publications

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“…Using geospatial modeling, Seng et al (2005) define dengue outbreak as the occurrence of more than one case in a specific geographical area where the onset date between cases is less than 14 days. Adopting the Apriori frequent mining approach, Long et al (2010) concluded that high volumes of records are not critical for outbreak detection since the complexity of the existing attribute values can determine the potential dengue outbreaks. The Control and Prevention of Vector Borne Diseases Program, Ministry of Health Malaysia adopts the operational definition for outbreak of dengue as the occurrence of more than one case of dengue in same locality, within the same incubation period of the first case or index case of dengue notified to the authorities.…”

Section: Related Work Dengue Outbreak Detectionmentioning

confidence: 99%

“…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%

“…The experiments have been conducted under different feature scheme, however the comparison indicate the applicability of using feature selection method that improves the modeling of dengue outbreak detection. The comparison includes the results from this research with three methods namely negative selection algorithm (NSA) by Mousavi et al (2011), multiple attribute value (MAV) by Long et al (2010) and cumulative sum (CUSUM) used by Long et al (2010) to compare their result. PSO generates the least number of features with the highest accuracy and able to optimize the number of features thus producing the highest detection accuracy.…”

Section: Performance Of Pso-based Technique Withmentioning

confidence: 99%

“…The early detection of dengue outbreak has been given the highest priority in previous works with the predictive accuracy achieved in the range of 70-80% (Long et al 2010;Mousavi et al 2011). Past research attempted to model the problem based on the historical data of dengue transitions using different classification techniques in order to build dengue outbreak predictive models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Related Work Dengue Outbreak Detectionmentioning

confidence: 99%

Section: Performance Of Pso-based Technique Withmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Husam¹,

Abuhamad²,

Bakar³

et al. 2017

JSM

Self Cite

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show abstract

“…The authors obtained an average prediction accuracy of 76%. Long et al proposed the multiple attribute value (MAV) method for dengue outbreak detection [16]. MAV is based on frequent mining analysis, for which the calculations are based on the frequent attribute elements within a dataset.…”

Section: Related Studiesmentioning

confidence: 99%

Negative selection algorithm for dengue outbreak detection

Mousavi

Bakar

Zainudin

et al. 2013

Turk J Elec Eng & Comp Sci

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Abstract:Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

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Outbreak detection model based on danger theory

Mohsin

Bakar

Hamdan

2014

Applied Soft Computing

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a b s t r a c tIn outbreak detection, one of the key issues is the need to deal with the weakness of early outbreak signals because this causes the detection model to have has less capability in terms of robustness when unseen outbreak patterns vary from those in the trained model. As a result, an imbalance between high detection rate and low false alarm rate occurs. To solve this problem, this study proposes a novel outbreak detection model based on danger theory; a bio-inspired method that replicates how the human body fights pathogens. We propose a signal formalization approach based on cumulative sum and a cumulative mature antigen contact value to suit the outbreak characteristic and danger theory. Two outbreak diseases, dengue and SARS, are subjected to a danger theory algorithm; namely the dendritic cell algorithm. To evaluate the model, four measurement metrics are applied: detection rate, specificity, false alarm rate, and accuracy. From the experiment, the proposed model outperforms the other detection approaches and shows a significant improvement for both diseases outbreak detection. The findings reveal that the robustness of the proposed immune model increases when dealing with inconsistent outbreak signals. The model is able to detect new unknown outbreak patterns and can discriminate between outbreak and non-outbreak cases with a consistent high detection rate, high sensitivity, and lower false alarm rate even without a training phase.

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Multiple Attribute Frequent Mining-Based for Dengue Outbreak

Cited by 7 publications

References 8 publications

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Feature Selection Algorithms for Malaysian Dengue Outbreak Detection Model

Negative selection algorithm for dengue outbreak detection

Outbreak detection model based on danger theory

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