Molecular epidemiology of dengue in North Kalimantan, a province with the highest incidence rates in Indonesia in 2019

Sasmono, R. Tedjo; Sutjianto, Angeline; Santoso, Marsha S.; Sriwedari, Karomah; Yohan, Benediktus; Mayasanti, Erna; Hayati, Rahma; Denis, Dionisius

doi:10.1016/j.meegid.2021.105036

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…The rapid spread of the dengue virus has become more and more dangerous, and addressing this issue should be considered an urgent case. Additionally, the national incidence of dengue hemorrhagic fever (DHF) in Indonesia increased from 50.8 per 100,000 population in 2015 to 78.9 per 100,000 population in 2016 [20]. The clinical diagnosis can range from symptomatic dengue fever (DF) to a more severe form known as DHF, and the most fatal is dengue shock syndrome (DSS) [18].…”

Section: Introductionmentioning

confidence: 99%

Dengue classification method using support vector machines and cross-validation techniques

Hamdani

Hatta

Puspitasari

et al. 2022

IJ-AI

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Dengue is a dangerous disease that can lead to death if the diagnosis and treatment are inappropriate. The common symptoms that occur, including headache, muscle aches, fever, and rash. Dengue is a disease that causes endemics in several countries in South Asia and Southeast Asia. There are three varieties of dengue, such as dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). This disease can currently be classified using a machine learning approach with the input data being the dengue symptoms. This study aims to classify dengue types consisting of three classes: DF, DHF, and DSS using five classification methods including C.45, decision tree (DT), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). The dataset used consists of 21 attributes, which are the dengue symptoms. It was collected from 110 patients. The evaluation method was conducted using cross-validation with k-folds of 3, 5, and 10. The dengue classification method was evaluated using three parameters: precision, recall, and accuracy, which were most optimally achieved. The most optimal evaluation results were obtained using SVM with k-fold 3 and 10 with precision, recall, and accuracy values reaching 99.1%, 99.1%, and 99.1%, respectively.

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Section: Introductionmentioning

confidence: 99%

Dengue classification method using support vector machines and cross-validation techniques

Hamdani

Hatta

Puspitasari

et al. 2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…The rapid spread of the dengue virus has grown increasingly harmful, and resolving this issue should be regarded as an emergency. Based on the statistics data, the national incidence of DHF in Indonesia increased from 50.8 per 100,000 people in 2015 to 78.9 per 100,000 people in 2016 [6]. The clinical diagnosis can range from symptomatic DF to DHF, the most severe variant, and DSS, the most lethal [4].…”

Section: Introductionmentioning

confidence: 99%

Expert System of Dengue Disease Using Artificial Neural Network Classifier

2022

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– Expert systems can be applied to the classification of dengue fever. Dengue is a serious disease that can be fatal if not diagnosed and treated properly. Headache, muscle aches, fever, and rash are some of the most prevalent symptoms. Dengue fever is a disease that is endemic in various South Asian and Southeast Asian nations. Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome are the three types of dengue (DSS). Currently, these diseases may be classified using a machine learning approach with dengue symptoms as the input data. This study proposes implementing an Artificial Neural Network (ANN) with the Backpropagation (BPNN) algorithm as the classifier to categorize dengue types, divided into three categories: DF, DHF, and DSS. The dengue symptoms are represented by 21 attributes in the dataset. It was gathered from 110 patients. Cross-validation with k-fold 3, 5, and 10 were applied as the evaluation method. Three parameters were obtained to evaluate the ANN classification method: precision, recall, and accuracy. These were used to justify the most optimal performance. Cross-validation using k-fold 3 produced the best evaluation results, with precision, recall, and accuracy values of 97.3%, 97.3%, and 97.27%, respectively.

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Dengue virus population genetics in Yogyakarta, Indonesia prior to city-wide Wolbachia deployment

Arguni

Indriani

Rahayu

et al. 2022

Infection, Genetics and Evolution

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Molecular epidemiology of dengue in North Kalimantan, a province with the highest incidence rates in Indonesia in 2019

Cited by 5 publications

References 43 publications

Dengue classification method using support vector machines and cross-validation techniques

Dengue classification method using support vector machines and cross-validation techniques

Expert System of Dengue Disease Using Artificial Neural Network Classifier

Dengue virus population genetics in Yogyakarta, Indonesia prior to city-wide Wolbachia deployment

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