Evaluation of Dengue Model Performances Developed Using Artificial Neural Network and Random Forest Classifiers

Silitonga, Permatasari; Dewi, Beti Ernawati; Bustamam, Alhadi; Al-Ash, Herley Shaori

doi:10.1016/j.procs.2020.12.018

Cited by 14 publications

(11 citation statements)

References 7 publications

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“…The prediction results of random forest classification and ordinary kriging are good enough to display in the form of a map. The random forest classification prediction model developed in this study has better performance than studies [8], [9], [10], and [11]. This is because the random forest model developed in this study applies feature expansion based on several previous years and obtained an accuracy value of 97% in model testing.…”

Section: Discussionmentioning

confidence: 81%

“…The decision tree was chosen randomly from the training data, then combined using the Breiman bagging method. After that, majority voting is carried out based on the decision tree to get predictive results [11].…”

Section: Random Forestmentioning

confidence: 99%

“…The performance of the Random Forest model has been tested in predicting and classifying various types of datasets, even for unbalanced classes [24]. This is influenced by the use of random sampling and the principle of the ensemble technique [11]. According to [25] the Random Forest algorithm can naturally adjust to unbalanced classes by down-sampling the majority class and constructing each tree for the minority class so that the dataset becomes more balanced.…”

Section: Random Forestmentioning

confidence: 99%

“…Some studies usually only focus on predicting or classifying DHF, but not based on the problem of spreading cases in an area. Prediction and classification of DHF have been carried out by several researchers [8], [9], [10], and [11]. The study [8] applied the Random Forest algorithm using 10-fold cross-validation to predict dengue fever based on patient data from hospitals and laboratories.…”

Section: Introductionmentioning

confidence: 99%

“…The built diagnostic system produces 91% accuracy, 91% recall, and 91% specificity. Furthermore, the study [11] applied the Random Forest algorithm and Artificial Neural Network (ANN) to predict the clinical degree of DHF. The data used comes from patient data and laboratory data.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features

Ashok

Prasetiyowati

Sibaroni

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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This study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time.

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Section: Discussionmentioning

confidence: 81%

Section: Random Forestmentioning

confidence: 99%