Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, EschsurAlzette, Luxembourg.
Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt.
Sci Rep. 2020 Mar 19;10(1):5058. doi: 10.1038/s41598-020-61853-y.
Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the outbreak incidence of VDPF requires an accurate analysis of the alarming data. The overarching aim to this study is to develop a novel hybrid machine learning approach to identify the key parameters that dominate the outbreak incidence of VDPV. The proposed method is based on the integration of random vector functional link (RVFL) networks with a robust optimization algorithm called whale optimization algorithm (WOA). WOA is applied to improve the accuracy of the RVFL network by finding the suitable parameter configurations for the algorithm. The classification performance of the WOA-RVFL method is successfully validated using a number of datasets from the UCI machine learning repository. Thereafter, the method is implemented to track the VDPV outbreak incidences recently occurred in several provinces in Lao People's Democratic Republic. The results demonstrate the accuracy and efficiency of the WOA-RVFL algorithm in detecting the VDPV outbreak incidences, as well as its superior performance to the traditional RVFL method.
最近，由于其严重后果，人们对疫苗衍生脊髓灰质炎病毒（VDPV）监测给予了极大关注。预测 VDPF 的爆发发病率需要对警示数据进行准确分析。本研究的总体目标是开发一种新的混合机器学习方法，以确定主导 VDPV 爆发发病率的关键参数。所提出的方法基于随机向量功能链接 (RVFL) 网络与称为鲸鱼优化算法 (WOA) 的强大优化算法的集成。WOA 通过为算法找到合适的参数配置来提高 RVFL 网络的准确性。使用 UCI 机器学习存储库中的多个数据集成功验证了 WOA-RVFL 方法的分类性能。然后，该方法用于跟踪最近在老挝人民民主共和国几个省份发生的 VDPV 爆发发病率。结果表明，WOA-RVFL 算法在检测 VDPV 爆发发病率方面具有准确性和效率，并且优于传统的 RVFL 方法。
