Detection of Dengue Disease Empowered with Fused Machine Learning

Rustom Al Nasar, Mohammad and Nasir, Iftikhar and Mohamed, Tamer and Elmitwally, Nouh and Al-Sakhnini, Mahmoud M. and Asgher, Tayba (2023) Detection of Dengue Disease Empowered with Fused Machine Learning. In: 2022 International Conference on Cyber Resilience (ICCR), 6th - 7th October 2022, Dubai, United Arab Emirates.

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Dengue fever is a life-threatening illness that affects both industrialized and poor nations, including Pakistan. It is necessary to forecast the illness at an early stage to avoid it. Machine Learning (ML) methods outperform other computer approaches in terms of illness prediction. The model utilized in this study to predict dengue fever is fused with machine learning. Artificial Neural Networks (ANN) and Support Vector Machine (SVM) provide the foundation of the conceptual framework. The datasets employed in these models have been collected from a government hospital in Lahore, Pakistan for diagnosing dengue fever (positive or negative). 70% of the statistics in the dataset are training data, whereas 30% are testing data. This fused model's membership functions explain whether a dengue diagnostic is positive or negative, which controls the model's output. A cloud storage system saves the fused model based on patients' real-time information for future use. The proposed model has a 96.19 % accuracy rate, which is much greater than earlier research.

Item Type: Conference or Workshop Item (Paper)
Identification Number:
6 October 2022Accepted
3 January 2023Published Online
Uncontrolled Keywords: Dengue Fever (DF), Dengue Hemorrhagic Fever (DHF), Dengue Prediction, Prediction Fused Dengue Model (PFDM)
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Nouh Elmitwally
Date Deposited: 22 Nov 2022 11:42
Last Modified: 20 Jan 2023 13:51

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