Research Article | Open Access | Download Full Text
Volume 1 | Issue 2 | Year 2022 | Article Id: DST-V1I2P101 DOI: https://doi.org/10.59232/DST-V1I2P101
An Optimal Prediction of Dengue Fever Based on Pso-Optimized Fuzzy-ELM
R.Surendiran
| Received | Revised | Accepted | Published |
|---|---|---|---|
| 28 Oct 2022 | 11 Nov 2022 | 19 Nov 2022 | 05 Dec 2022 |
Citation
R.Surendiran. “An Optimal Prediction of Dengue Fever Based on Pso-Optimized Fuzzy-ELM.” DS Journal of Digital Science and Technology, vol. 1, no. 2, pp. 1-10, 2022.
Abstract
Dengue is an illness caused by dengue viruses (DENVs) carried by Aedes mosquitoes. Dengue hemorrhagic fever can develop into life-threatening dengue shock syndrome. To provide timely supportive care and therapy, it is necessary to have indispensable practical instruments that accurately differentiate dengue and its types in the early stages of illness advancement. Due to a scarcity of vaccines and medications, early detection of a dengue outbreak is critical to lowering the number of deaths. Several computer vision-based studies have been conducted in recent years to recognize DENV and the stages of fever. However, the existing standard machine learning techniques face challenges of inaccurate prediction, while backpropagation neural yields sufficient results, yet it requires very large training data and has slow consolidation. To overcome the existing challenges, this paper proposes a novel machine learning (ML) model based on PSO-FELM (PSO optimized Fuzzy-ELM) to predict dengue fever. First, the collected data is pre-processed to transform raw data into a beneficial and efficient format (cleaned, standardized, and noise-free). Then Fuzzy-ELM based machine learning framework is presented for optimally predicting dengue fever. Herein fuzzy logic is applied to overcome the imbalance and weighted classification problems. PSO is employed to obtain the optimal parameters, which assist to improves the diagnostic accuracy of FELM. The goal of this study is to help patients diagnose dengue illness on their own. It allows patients to consult with an expert and reduces the medical examiner's workload in advance. When compared to existing methodologies such as ELM, FELM, PSO-ELM, and PSO, the research shows that the suggested model can attain 95 percent accuracy
Keywords
Dengue, Viruses, Aedes mosquitoes, PSO-FELM, Fuzzy-ELM
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