A comparison of Montecarlo linear and dynamic polynomial regression in predicting dengue fever case

Roziqin, Mochammad Choirur; Basuki, Achmad; Harsono, Tri

doi:10.1109/kcic.2016.7883649

Cited by 14 publications

(19 citation statements)

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“…The prediction of dengue outbreaks is crucial worldwide because this infectious disease remains as a major issue in many countries [11,26,30,31]. Table 1 lists studies on different models of dengue outbreak prediction with distinct climatic risk factors.…”

Section: Different Models Of Dengue Outbreak Prediction Systems In Mamentioning

confidence: 99%

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Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2019

Preprint

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Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. These factors were used as input parameters for machine learning models. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks.

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Section: Different Models Of Dengue Outbreak Prediction Systems In Mamentioning

confidence: 99%

“…The asterisk (*) in the columns of the table denotes the risk factors used in different studies. shown that temperature and rainfall directly and significantly affect dengue outbreaks [14,20,26,30,31].…”

Section: Different Models Of Dengue Outbreak Prediction Systems In Mamentioning

confidence: 99%

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2019

Preprint

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“…Vulnerability maps of dengue incidences have been generated in Malaysia, resulting in the development and implementation of visualised and predictive modelling using geographic information systems (GIS) for dengue fever in Selangor, Malaysia [28]. There are different models of dengue outbreak prediction systems in Malaysia have achieved different accuracies [16,25].In 2015, [29] predicted localised dengue incidences in Malaysia using an ensemble system for identi cation and found that ensemble models exhibit better prediction power than a single model [29].The prediction of dengue outbreaks is crucial worldwide because this infectious disease remains as a major issue in many countries [14,26,30,31]. Table 1 lists studies on different models of dengue outbreak prediction with distinct climatic risk factors.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies on dengue fever were conducted in Asian countries, such as Malaysia, Singapore, Taiwan, Indonesia, Bangladesh and Thailand, are critical areas for dengue fever. Most studies have shown that temperature and rainfall directly and signi cantly affect dengue outbreaks [15,18,25,26,30,31].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, changing climatic factors, such as increasing temperature, rainfall and humidity, are the most in uential driving forces of dengue virus transmission [31]. One study correlated dengue cases with climatic variables in the city of Singapore and the model for dengue cases was considered the dependent variable; meanwhile, climatic variables, such as rainfall, maximum and minimum temperatures and relative humidity, were considered independent variables [27].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Nejad

Varathan

2020

Preprint

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Background: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50–100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. Methods: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of four-years data (January 2010 to December 2013) collected in Malaysia. Results: This research has two major contributions. A new risk factor, called the TempeRain Factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% and reduced the root-mean-square error to 0.26 for predicting dengue outbreaks. Conclusions: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.

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Prediction of Seasonal Ailments Using Big Data

Indhumathi

Kumar

2023

Mathematics and Computer Science Volume 1

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A comparison of Montecarlo linear and dynamic polynomial regression in predicting dengue fever case

Cited by 14 publications

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Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Identification of Significant Climatic Risk Factors and Machine Learning Models in Dengue Outbreak Prediction

Prediction of Seasonal Ailments Using Big Data

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