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
Department of Electronics and Instrumentation Engineering, National Engineering College, Kovilpatti, Nallatinputhur, Tamil Nadu 628503, India.
Department of Information Technology, Panimalar Institute of Technology, Chennai, Tamil Nadu 600123, India.
Evid Based Complement Alternat Med. 2022 Jun 9;2022:5669580. doi: 10.1155/2022/5669580. eCollection 2022.
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.
在流行地区进行登革热建模对于减少疫情爆发和改善病媒传播疾病控制至关重要。由于缺乏治疗方法和通用疫苗，登革热的早期预测是疾病控制的关键工具。神经网络已通过多种方式为公共卫生做出了重大贡献。在本文中，我们开发了一种使用随机森林（RF）的深度学习模型，该模型有助于从文本数据集中提取登革热的特征。所提出的模型涉及数据收集、输入文本的预处理和特征提取。对提取的特征进行研究，以测试使用随机森林进行特征提取在登革热数据集上的有效性。模拟结果表明，所提出的方法在从输入数据集中提取特征方面比其他特征提取方法具有更高的准确率，比现有方法提高了超过12%。此外，该研究减少了与特征提取相关的误差，比其他现有方法少10%，这表明了该模型的有效性。
