The Significance of Software Engineering to Forecast the Public Health Issues
A Case of Saudi Arabia
Bibliographic Data
| ID | 22077450 |
|---|---|
| Authors | Haneen Hassan Al-Ahmadi |
| Year | 2022 |
| Volume | 10 |
| Publication date | 2022-08-18 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.900075 |
| Language | EN |
| References cited | 51 |
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
Africa Development Indicators 2012/2013
Epidemiological time series studies of PM 2.5 and daily mortality and hospital admissions
Environmental and Health Impacts of Air Pollution
Oil sector and CO2 emissions in Saudi Arabia
Indoor air pollution (PM 2.5 ) due to secondhand smoke in selected hospitality and entertainment venues of Karachi, Pakistan
Revisiting the empirical relationship among the main targets of sustainable development
Causes and health consequences of environmental degradation and social injustice
Ecology of Increasing Diseases
| Highly cited | No |
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