Recurrent Neural Networks for Feature Extraction from Dengue Fever

• Published in: Evidence-based complementary and alternative medicine Vol. 2022; pp. 1 - 9
• Main Authors: Daniel, Jackson, Irin Sherly, S., Ponnuramu, Veeralakshmi, Pratap Singh, Devesh, Netra, S.N., Alonazi, Wadi B., Almutairi, Khalid M.A., Priyan, K.S.A., Abera, Yared
• Format: Journal Article
• Language: English
• Published: New York Hindawi 09-06-2022; Hindawi Limited
• Subjects: Artificial intelligence; Classification; Datasets; Decision trees; Deep learning; Dengue fever; Disease control; Epidemics; Feature selection; Fever; Infections; Machine learning; Neural networks; Patients; Pest outbreaks; Public health; Tropical diseases; Vaccination; Zika virus
• ISSN: 1741-427X; 1741-4288
• DOI: 10.1155/2022/5669580

Dengue fever modelling in endemic locations is critical to reducing outbreaks and improving vector-borne illness control. Early projections of dengue are a crucial tool for disease control because of the unavailability of treatments and universal vaccination. Neural networks have made significant contributions to public health in a variety of ways. In this paper, we develop a deep learning modelling using random forest (RF) that helps extract the features of the dengue fever from the text datasets. The proposed modelling involves the data collection, preprocessing of the input texts, and feature extraction. The extracted features are studied to test how well the feature extraction using RF is effective on dengue datasets. The simulation result shows that the proposed method achieves higher degree of accuracy that offers an improvement of more than 12% than the existing methods in extracting the features from the input datasets than the other feature extraction methods. Further, the study reduces the errors associated with feature extraction that is 10% lesser than the other existing methods, and this shows the efficacy of the model.