In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation between COVID-19 and other infectious diseases. We explored the use of AI-powered predictive models and classifiers to enhance healthcare preparedness by forecasting respiratory disease trends using COVID-19 data. We developed mathematical models based on autoregressive (AR), moving average (MA), ARMA, and machine and deep learning algorithms to predict daily confirmed deaths. Statistical models were trained on 80% of the data and tested on the remaining 20%, with predicted results compared to actual values. The ARMA model demonstrated promising performance. Additionally, eight machine learning (ML) classifiers and deep learning (DL) models were utilized to identify COVID-19 severity indicators. Among the ML classifiers, the Decision Tree (DT) achieved the highest accuracy at 74.70%, followed closely by Random Forest (RF) at 74.66%. DL models showed comparable accuracy scores, around 70%. In terms of AUC-ROC, the kernel Support Vector Machine (SVM) outperformed others, achieving 71% accuracy, with precision, recall, F-measure, and area under the curve values of 0.7, 0.75, 0.59, and 0.72, respectively. These findings underscore the potential of AI-driven health analysis to optimize resource allocation and enhance forecasting for respiratory diseases.
Citation: Saleh I. Alzahrani, Wael M. S. Yafooz, Ibrahim A. Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh. AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data[J]. Mathematical Biosciences and Engineering, 2025, 22(3): 554-584. doi: 10.3934/mbe.2025021
In low-income and resource-limited countries, distinguishing COVID-19 from other respiratory diseases is challenging due to similar symptoms and the prevalence of comorbidities. In Yemen, acute comorbidities further complicate the differentiation between COVID-19 and other infectious diseases. We explored the use of AI-powered predictive models and classifiers to enhance healthcare preparedness by forecasting respiratory disease trends using COVID-19 data. We developed mathematical models based on autoregressive (AR), moving average (MA), ARMA, and machine and deep learning algorithms to predict daily confirmed deaths. Statistical models were trained on 80% of the data and tested on the remaining 20%, with predicted results compared to actual values. The ARMA model demonstrated promising performance. Additionally, eight machine learning (ML) classifiers and deep learning (DL) models were utilized to identify COVID-19 severity indicators. Among the ML classifiers, the Decision Tree (DT) achieved the highest accuracy at 74.70%, followed closely by Random Forest (RF) at 74.66%. DL models showed comparable accuracy scores, around 70%. In terms of AUC-ROC, the kernel Support Vector Machine (SVM) outperformed others, achieving 71% accuracy, with precision, recall, F-measure, and area under the curve values of 0.7, 0.75, 0.59, and 0.72, respectively. These findings underscore the potential of AI-driven health analysis to optimize resource allocation and enhance forecasting for respiratory diseases.
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Figures(14) / Tables(8)
Saleh I. Alzahrani, Wael M. S. Yafooz, Ibrahim A. Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh. AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data[J]. Mathematical Biosciences and Engineering, 2025, 22(3): 554-584. doi: 10.3934/mbe.2025021
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