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. 2020 Mar 19;10(1):5058.
doi: 10.1038/s41598-020-61853-y.

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach

Ahmed A Hemedan #  1 , Mohamed Abd Elaziz #  2   3 , Pengcheng Jiao  4 , Amir H Alavi  5   6   7 , Mahmoud Bahgat  8   9 , Marek Ostaszewski  1 , Reinhard Schneider  1 , Haneen A Ghazy  10 , Ahmed A Ewees  11 , Songfeng Lu  12
Affiliations

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach

Ahmed A Hemedan et al. Sci Rep. .

Erratum in

Abstract

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Structure of the RVFL network.
Algorithm 1
Algorithm 1
The Whale Optimization Algorithm.
Figure 2
Figure 2
The WOA-RVFL classification process.
Figure 3
Figure 3
Accuracy, Precision and Recall rates of the WOA-RVFL and RVFL methods for the training set (UCI data).
Figure 4
Figure 4
Accuracy, Precision and Recall rates of the WOA-RVFL and RVFL methods for the validation set (UCI data).
Figure 5
Figure 5
Accuracy, Precision, and Recall of the WOA-RVFL and RVFL methods for the testing set (UCI data).
Figure 6
Figure 6
The correlation between the parameters included in the model development.
See this image and copyright information in PMC

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