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Case Reports
. 2022 Aug 18:10:900075.
doi: 10.3389/fpubh.2022.900075. eCollection 2022.

The Significance of Software Engineering to Forecast the Public Health Issues: A Case of Saudi Arabia

Haneen Hassan Al-Ahmadi  1
Affiliations
Case Reports

The Significance of Software Engineering to Forecast the Public Health Issues: A Case of Saudi Arabia

Haneen Hassan Al-Ahmadi. Front Public Health. .

Abstract

In the recent years, public health has become a core issue addressed by researchers. However, because of our limited knowledge, studies mainly focus on the causes of public health issues. On the contrary, this study provides forecasts of public health issues using software engineering techniques and determinants of public health. Our empirical findings show significant impacts of carbon emission and health expenditure on public health. The results confirm that support vector machine (SVM) outperforms the forecasting of public health when compared to multiple linear regression (MLR) and artificial neural network (ANN) technique. The findings are valuable to policymakers in forecasting public health issues and taking preemptive actions to address the relevant health concerns.

Keywords: Saudi Arabia; artificial neural network; forecasting; public health; support vector machine.

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Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Population rising trend (1996–2019) (Source: world development indicators).
Figure 2
Figure 2
Death rate in Saudi Arabia (per 100,000) in 2019 (Source: world development indicators).
Figure 3
Figure 3
Architecture of artificial neural network (ANN).
Figure 4
Figure 4
The proposed steps for the methodology of this study.
Figure 5
Figure 5
Health issue since 2006–2020.
Figure 6
Figure 6
Plot of health issues and prediction of testing data.
Figure 7
Figure 7
The graph shows the real health issues and forecasts from multiple linear regression (MLR), ANN, and support vector regression (SVR) for training data.
Figure 8
Figure 8
The percentage errors of MLR, ANN, and SVR models for testing data.
Figure 9
Figure 9
The graph shows the real health issues and forecasts from MLR, ANN, and SVR for testing data.
Figure 10
Figure 10
Forecasted health issues from October 2018 to October 2019.
See this image and copyright information in PMC

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