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All Journal JOIV : International Journal on Informatics Visualization
Zukarnain, Zuriani Ahmad
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Computer Science & IT

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A scoping review and bibliometric analysis (ScoRBA) on dengue infection and machine learning research Zahiruddin, Haikal; Zukarnain, Zuriani Ahmad; Wijaya, Adi
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2249

Abstract

Dengue, a fast-spreading vector-borne infectious disease, requires early prediction and prompt decision-making for effective control. To address this issue, we present a comprehensive scoping review and bibliometric analysis (ScoRBA) that aims to map the current literature landscape, identify main research themes, and offer valuable insights into advancements and challenges in dengue infection and machine learning research. Materials for this analysis consist of scholarly articles related to dengue and machine learning research retrieved from the Scopus database. Our method involves a rigorous literature examination, utilizing keyword co-occurrence analysis. Our study reveals a growing interest in dengue and machine learning research, reflected in an increasing number of publications. Through keyword co-occurrence analysis, we identify four major research themes: Data mining using machine learning for dengue prediction, Deep learning approach for dengue prediction models, Neural network optimization for dengue diagnostic systems, and Climate-driven dengue prediction with IoT & remote sensing. Advancements include substantial improvements in prediction models through machine learning and IoT integration, albeit with identified limitations, necessitating ongoing research and refinement. Our findings hold direct implications for public health professionals, academics, and decision-makers, offering data-driven strategies for dengue outbreak control. The identified research themes act as a roadmap for future investigations, guiding the development of more robust tools for early prediction and decision-making in the battle against dengue. This study contributes to understanding the evolving landscape of dengue research, facilitating informed actions to mitigate the impact of this infectious disease. 
Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models Muhamad Krishnan, Nor Farisha; Zukarnain, Zuriani Ahmad; Ahmad, Azlin; Jamaludin, Marhainis
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.6.3.788

Abstract

Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change.
Co-Authors Adi Wijaya Ahmad, Azlin Jamaludin, Marhainis Muhamad Krishnan, Nor Farisha Zahiruddin, Haikal