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. 2023 May 6;13(9):1641.
doi: 10.3390/diagnostics13091641.

A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm

Thandra Jithendra  1 , Shaik Sharief Basha  1
Affiliations

A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm

Thandra Jithendra et al. Diagnostics (Basel). .

Abstract

This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data.

Keywords: COVID-19 influenza; coefficient of determination; fuzzy logic; nature-inspired algorithms.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Dissemination analysis of confirmed cases in China and India.
Figure 2
Figure 2
Architecture of ANFIS model.
Figure 3
Figure 3
Flow chart of enhanced model ANFIS-RSA.
Figure 4
Figure 4
A bar graph depicting the rise in daily cases reported in China.
Figure 5
Figure 5
Convergence speed of trained ANFIS-RSA vs. ANFIS-CESBAS models for China.
Figure 6
Figure 6
Graphical comparison of ANFIS-RSA and ANFIS-CESBAS in the form of bar plots of RMSE values of 10 independent runs.
Figure 7
Figure 7
Graphical comparison of ANFIS-RSA and ANFIS-CESBAS in the form of bar plots of MAE values of 10 independent runs.
Figure 8
Figure 8
ANFIS-RSA forecast COVID-19 infections for China.
Figure 9
Figure 9
ANFIS-CESBAS forecast COVID-19 infections for China.
Figure 10
Figure 10
Comparative analysis of ANFIS-RSA vs. ANFIS-CESBAS predictions for China.
Figure 11
Figure 11
Confirmed COVID-19 cases in India from 3 November 2021 to 21 January 2022.
Figure 12
Figure 12
Convergence speed of trained ANFIS-RSA vs. ANFIS-CESBAS models for India.
Figure 13
Figure 13
Comparative study of ANFIS-RSA vs. ANFIS-CESBAS of RMSE values on 10 runs.
Figure 14
Figure 14
Comparative analysis of ANFIS-RSA vs. ANFIS-CESBAS of MAE values on 10 runs.
Figure 15
Figure 15
COVID-19 cases forecasted by ANFIS-RSA from 3 November 2021 to 21 January 2022.
Figure 16
Figure 16
COVID-19 cases forecasted by ANFIS-CESBAS from 3 November 2021, to 21 January 2022.
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