The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review
This systematic review analyzes the implementation and effectiveness of machine learning (ML) approaches for infectious disease surveillance and prediction across the Middle East and North Africa (MENA) region. Adhering to PRISMA guidelines, studies published between 2016 and 2024 were examined to a...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Informatics in Medicine Unlocked |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914825000395 |
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| author | Radwan Qasrawi Ghada Issa Suliman Thwib Razan AbuGhoush Malak Amro Raghad Ayyad Stephanny Vicuna Eman Badran Yousef Khader Raeda Al Qutob Faris Al Bakri Hana Trigui Elie Sokhn Emmanuel Musa Jude Dzevela Kong |
| author_facet | Radwan Qasrawi Ghada Issa Suliman Thwib Razan AbuGhoush Malak Amro Raghad Ayyad Stephanny Vicuna Eman Badran Yousef Khader Raeda Al Qutob Faris Al Bakri Hana Trigui Elie Sokhn Emmanuel Musa Jude Dzevela Kong |
| author_sort | Radwan Qasrawi |
| collection | DOAJ |
| description | This systematic review analyzes the implementation and effectiveness of machine learning (ML) approaches for infectious disease surveillance and prediction across the Middle East and North Africa (MENA) region. Adhering to PRISMA guidelines, studies published between 2016 and 2024 were examined to assess model structures, performance metrics, and dataset characteristics. The findings reveal a predominance of deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieving mean accuracy rates of 96.3 % in pathogen detection from medical imaging. Random Forest algorithms demonstrated superior performance in disease outbreak prediction, with mean ACC scores of 0.85. Iran, Saudi Arabia, and Egypt emerged as regional leaders, collectively contributing 54 % of the analyzed studies. The temporal analysis showed peak research output in 2022 (n = 30 studies), followed by a 25 % decline in 2023. Despite promising performance, challenges such as data quality, infrastructural limitations, and algorithmic bias persist. This review highlights the need for standardized protocols, enhanced digital infrastructure, and collaborative efforts to realize the full potential of ML in enhancing public health interventions across the region. Future research directions should prioritize multi-center validation studies, standardized reporting frameworks, and integration of diverse data modalities to enhance model robustness and clinical applicability. |
| format | Article |
| id | doaj-art-9e7371cdeb8c4a2285d4a289c3f0e0e3 |
| institution | Kabale University |
| issn | 2352-9148 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj-art-9e7371cdeb8c4a2285d4a289c3f0e0e32025-08-20T03:44:55ZengElsevierInformatics in Medicine Unlocked2352-91482025-01-015610165110.1016/j.imu.2025.101651The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic reviewRadwan Qasrawi0Ghada Issa1Suliman Thwib2Razan AbuGhoush3Malak Amro4Raghad Ayyad5Stephanny Vicuna6Eman Badran7Yousef Khader8Raeda Al Qutob9Faris Al Bakri10Hana Trigui11Elie Sokhn12Emmanuel Musa13Jude Dzevela Kong14Department of Computer Science, Al Quds University, Jerusalem, Palestine; Department of Computer Engineering, Istinye University, Istanbul, Turkey; Corresponding author. Department of Computer Science, Al Quds University, Ramallah, Palestine,.Department of Computer Science, Al Quds University, Jerusalem, PalestineDepartment of Computer Science, Al Quds University, Jerusalem, PalestineDepartment of Computer Science, Al Quds University, Jerusalem, PalestineDepartment of Computer Science, Al Quds University, Jerusalem, PalestineDepartment of Computer Science, Al Quds University, Jerusalem, PalestineDepartment of Computer Science, Al Quds University, Jerusalem, PalestinePediatric department, Neonatal-Perinatal division, University of Jordan, Faculty of Medicine, JordanDepartment of Public Health, Faculty of Medicine, Jordan University of Science and Technology, JordanDept of Community and Family Medicine, University of Jordan, JordanPediatric department, Neonatal-Perinatal division, University of Jordan, Faculty of Medicine, JordanInstitut Pasteur de Tunis, the University of Tunis El Manar, TunisiaMolecular Testing Laboratory, Medical Laboratory Department, Faculty of Health Sciences, Beirut Arab University, Beirut, LebanonAI4PEP York University, Toronto, CanadaDepartment of Mathematics and Statistics, Faculty of Science, York University, CanadaThis systematic review analyzes the implementation and effectiveness of machine learning (ML) approaches for infectious disease surveillance and prediction across the Middle East and North Africa (MENA) region. Adhering to PRISMA guidelines, studies published between 2016 and 2024 were examined to assess model structures, performance metrics, and dataset characteristics. The findings reveal a predominance of deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieving mean accuracy rates of 96.3 % in pathogen detection from medical imaging. Random Forest algorithms demonstrated superior performance in disease outbreak prediction, with mean ACC scores of 0.85. Iran, Saudi Arabia, and Egypt emerged as regional leaders, collectively contributing 54 % of the analyzed studies. The temporal analysis showed peak research output in 2022 (n = 30 studies), followed by a 25 % decline in 2023. Despite promising performance, challenges such as data quality, infrastructural limitations, and algorithmic bias persist. This review highlights the need for standardized protocols, enhanced digital infrastructure, and collaborative efforts to realize the full potential of ML in enhancing public health interventions across the region. Future research directions should prioritize multi-center validation studies, standardized reporting frameworks, and integration of diverse data modalities to enhance model robustness and clinical applicability.http://www.sciencedirect.com/science/article/pii/S2352914825000395Artificial intelligence (AI)Machine learning (ML)infectious diseasesEarly detectionOutbreak predictionMedical imaging |
| spellingShingle | Radwan Qasrawi Ghada Issa Suliman Thwib Razan AbuGhoush Malak Amro Raghad Ayyad Stephanny Vicuna Eman Badran Yousef Khader Raeda Al Qutob Faris Al Bakri Hana Trigui Elie Sokhn Emmanuel Musa Jude Dzevela Kong The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review Informatics in Medicine Unlocked Artificial intelligence (AI) Machine learning (ML) infectious diseases Early detection Outbreak prediction Medical imaging |
| title | The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review |
| title_full | The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review |
| title_fullStr | The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review |
| title_full_unstemmed | The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review |
| title_short | The role of machine learning in infectious disease early detection and prediction in the MENA region: A systematic review |
| title_sort | role of machine learning in infectious disease early detection and prediction in the mena region a systematic review |
| topic | Artificial intelligence (AI) Machine learning (ML) infectious diseases Early detection Outbreak prediction Medical imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2352914825000395 |
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