Dengue fatality prediction using data mining

Rahim, Nur Fazliana; Taib, Shakirah Mohd; Abidin, Azwar

doi:10.4314/jfas.v9i6s.52

“…In summary, data mining in healthcare is used to evaluate the effectiveness of treatments, manage healthcare, manage customer relationships, and detect fraud and abuse, among other applications [39]. In addition, previous research has demonstrated the application of data mining techniques for predictive tasks, including HIV testing [35], cancer [40], heart disease [41], tuberculosis [42], kidney dialysis [43], diabetes [44], dengue fever [45], hepatitis C [46], and IVF [47].…”

Section: Applications Of Predictive Models In Healthcare Industrymentioning

confidence: 99%

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

Hailu,

Edris

2023

IIM

1

0

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In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model.

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“…In summary, data mining in healthcare is used to evaluate the effectiveness of treatments, manage healthcare, manage customer relationships, and detect fraud and abuse, among other applications [39]. In addition, previous research has demonstrated the application of data mining techniques for predictive tasks, including HIV testing [35], cancer [40], heart disease [41], tuberculosis [42], kidney dialysis [43], diabetes [44], dengue fever [45], hepatitis C [46], and IVF [47].…”

Section: A Applications Of Predictive Models In Healthcare Industrymentioning

confidence: 99%

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

Hailu

¹

,

Abdulkadir²

2023

Preprint

0

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In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user's requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

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“… 16 , 17 , 18 The deadly global DF outbreak that began in 2019 has also increased the usage of data science in the healthcare industry. 19 Researchers have utilized a variety of data science approaches, including retrospective and prospective analyses, to try to make sense of the global spread of the DENV. 20 , 21 , 22 Epidemiology can be thought of as the study of disease occurrence and transmission to identify risk factors.…”

Section: Introductionmentioning

confidence: 99%

“…Problems with crop harvesting, identifying disease trends, commercial data mining, and e‐commerce fraud are just some examples of the many areas where data science methods have been applied 16–18 . The deadly global DF outbreak that began in 2019 has also increased the usage of data science in the healthcare industry 19 . Researchers have utilized a variety of data science approaches, including retrospective and prospective analyses, to try to make sense of the global spread of the DENV 20–22 .…”

Section: Introductionmentioning

confidence: 99%

Dengue fever mapping in Bangladesh: A spatial modeling approach

Sarker,

Karim,

E‐Barket

et al. 2024

Health Science Reports

0

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BackgroundEpidemics of the dengue virus can trigger widespread morbidity and mortality along with no specific treatment. Examining the spatial autocorrelation and variability of dengue prevalence throughout Bangladesh's 64 districts was the focus of this study.MethodsThe spatial autocorrelation is evaluated with the help of Moran and Geary . Local Moran was used to detect hotspots and cold spots, whereas local Getis Ord G was used to identify only spatial hotspots. The spatial heterogeneity has been detected using various conventional and spatial models, including the Poisson‐Gamma model, the Poisson‐Lognormal Model, the Conditional Autoregressive (CAR) model, the Convolution model, and the BYM2 model, respectively. These models are implemented using Gibbs sampling and other Bayesian hierarchical approaches to analyze the posterior distribution effectively, enabling inference within a Bayesian context.ResultsThe study's findings show that Moran and Geary analysis provides a substantial clustering pattern of positive spatial autocorrelation of dengue fever (DF) rates between surrounding districts at a 90% confidence interval. The Local Indicators of Spatial Autocorrelation cluster mapped spatial clusters and outliers based on prevalence rates, while the local Getis‐Ord G displayed a thorough breakdown of high or low rates, omitting outliers. Although Chattogram had the most dengue cases (15,752), Khulna district had a higher prevalence rate (133.636) than Chattogram (104.796). The BYM2 model, determined to be well‐fitted based on the lowest Deviance Information Criterion value (527.340), explains a significant association between spatial heterogeneity and prevalence rates.ConclusionThis research pinpoints the district with the highest prevalence rate for dengue and the neighboring districts that also have high risk, allowing government agencies and communities to take the necessary precautions to mollify the risk effect of DF.

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Dengue fatality prediction using data mining

Cited by 8 publications

References 8 publications

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

Dengue fever mapping in Bangladesh: A spatial modeling approach

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