Abstract：
UNASSIGNED: Dengue fever is rapidly becoming Malaysia\'s most pressing health concern, as the reported cases have nearly doubled over the past decade. Without efficacious antiviral medications, vector control remains the primary strategy for battling dengue, while the recently introduced tetravalent immunization is being evaluated. The most significant and dangerous risk increasing recently is vector-borne illnesses. These illnesses induce significant human sickness and are transmitted by blood-feeding arthropods such as fleas, parasites, and mosquitos. A thorough grasp of various factors is necessary to improve prediction accuracy and typically generate inaccurate and unstable predictions, as well as machine learning (ML) models, weather-driven mechanisms, and numerical time series.
UNASSIGNED: In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm.
UNASSIGNED: The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model\'s architecture to increase forecast accuracy.
UNASSIGNED: RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods.
UNASSIGNED: According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.
UNASSIGNED: In this research, we propose a novel method for forecasting vector-borne disease risk using Radial Basis Function Networks (RBFNs) and the Darts Game Optimizer (DGO) algorithm.
UNASSIGNED: The proposed approach entails training the RBFNs with historical disease data and enhancing their parameters with the DGO algorithm. To prepare the RBFNs, we used a massive dataset of vector-borne disease incidences, climate variables, and geographical data. The DGO algorithm proficiently searches the RBFN parameter space, fine-tuning the model\'s architecture to increase forecast accuracy.
UNASSIGNED: RBFN-DGO provides a potential method for predicting vector-borne disease risk. This study advances predictive demonstrating in public health by shedding light on effectively controlling vector-borne diseases to protect human populations. We conducted extensive testing to evaluate the performance of the proposed method to standard optimization methods and alternative forecasting methods.
UNASSIGNED: According to the findings, the RBFN-DGO model beats others in terms of accuracy and robustness in predicting the likelihood of vector-borne illness occurrences.
摘要：
登革热正迅速成为马来西亚最紧迫的健康问题，在过去十年中，报告的病例几乎翻了一番。没有有效的抗病毒药物，媒介控制仍然是抗击登革热的主要策略，而最近引入的四价免疫正在评估中。最近增加的最重要和最危险的风险是媒介传播的疾病。这些疾病引起重大的人类疾病，并通过跳蚤等吸血节肢动物传播，寄生虫,还有蚊子.要全面掌握各种因素,提高预测精度,通常会产生不准确、不稳定的预测,以及机器学习(ML)模型，天气驱动机制，和数值时间序列。
■在这项研究中，我们提出了一种使用径向基函数网络（RBFN）和飞镖游戏优化器（DGO）算法预测媒介传播疾病风险的新方法。
■所提出的方法需要用历史疾病数据训练RBFN并用DGO算法增强它们的参数。为了准备RBFN，我们使用了大量的媒介传播疾病发病率数据集，气候变量,和地理数据。DGO算法熟练地搜索RBFN参数空间,微调模型的架构，以提高预测准确性。
■RBFN-DGO提供了一种预测媒介传播疾病风险的潜在方法。这项研究通过阐明有效控制媒介传播疾病以保护人群，从而促进了公共卫生的预测性证明。我们进行了广泛的测试，以评估所提出的方法与标准优化方法和替代预测方法的性能。
■根据调查结果，RBFN-DGO模型在预测媒介传播疾病发生可能性的准确性和稳健性方面优于其他模型.
■在这项研究中，我们提出了一种使用径向基函数网络（RBFN）和飞镖游戏优化器（DGO）算法预测媒介传播疾病风险的新方法。
■所提出的方法需要用历史疾病数据训练RBFN并用DGO算法增强它们的参数。为了准备RBFN，我们使用了大量的媒介传播疾病发病率数据集，气候变量,和地理数据。DGO算法熟练地搜索RBFN参数空间,微调模型的架构，以提高预测准确性。
■RBFN-DGO提供了一种预测媒介传播疾病风险的潜在方法。这项研究通过阐明有效控制媒介传播疾病以保护人群，从而促进了公共卫生的预测性证明。我们进行了广泛的测试，以评估所提出的方法与标准优化方法和替代预测方法的性能。
■根据调查结果，RBFN-DGO模型在预测媒介传播疾病发生可能性的准确性和稳健性方面优于其他模型.
