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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system’s efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.

Details

Title
DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction
Author
Kukkar, Ashima 1   VIAFID ORCID Logo  ; Kumar, Yugal 2 ; Sandhu, Jasminder Kaur 3   VIAFID ORCID Logo  ; Kaur, Manjit 4 ; Tarandeep Singh Walia 5   VIAFID ORCID Logo  ; Amoon, Mohammed 6   VIAFID ORCID Logo 

 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India; [email protected] 
 Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan 173234, India; [email protected] 
 Department of Computer Science & Engineering, Chandigarh University, Gharuan, Mohali 140413, India 
 School of Computer Science and Artificial Intelligence, SR University, Warangal 506371, India; [email protected] 
 School of Computer Application, Lovely Professional University, Phagwara 144411, India; [email protected] 
 Department of Computer Science, Community College, King Saud University, P.O. Box 28095, Riyadh 11437, Saudi Arabia 
First page
624
Publication year
2024
Publication date
2024
Publisher
MDPI AG
e-ISSN
20754418
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2991373273
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.