Detection of Dengue Disease Empowered with Fused Machine Learning

Al Nasar, Mohammad Rustom
Nasir, Iftikhar
Mohamed, Tamer
Elmitwally, Nouh Sabri
Al-Sakhnini, Mahmoud M.
Asgher, Tayba
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Institute of Electrical and Electronics Engineers Inc.
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. © 2022 IEEE.
This work is not available in the CUD collection. The version of the scholarly record of this conference paper is published in the 2022 International Conference on Cyber Resilience (ICCR) (2022), available online at:
Dengue Fever (DF) , Dengue Hemorrhagic Fever (DHF) , Dengue Prediction , Prediction Fused Dengue Model (PFDM)
Al Nasar, M. R., Nasir, I., Mohamed, T., Elmitwally, N. S., Al-Sakhnini, M. M., & Asgher, T. (2022). Detection of dengue disease empowered with fused machine learning. In 2022 International Conference on Cyber Resilience (ICCR), pp. 01-10,