Volume 8 • Issue 1 • PP: 06–14 • 2024
A Comprehensive Review on Optimizing Machine Learning Models for Early Detection and Forecasting of Monkeypox Outbreaks
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© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This is a significant problem in diagnosing zoonotic opportunistic ‘emerging’ diseases like Monkeypox, which require not only better diagnostics but also efficient, effective, and affordable diagnostics. This paper considers the possibilities of machine learning (ML), deep learning (DL), and optimization algorithms for diagnosing and predicting Monkeypox. The presently employed strategies can be enhanced because clinical and imaging data can be harnessed to drive these technologies for early detection and subsequent containment activities. Generally, in a review, the authors offer information on how the diagnostic processes using ML and DL result in enhanced accuracy, specificity, and sensitivity of models, thus reducing design reliabilities. Furthermore, outbreak data is subjected to predictive modeling analysis to establish patterns useful in helping risk managers and policymakers prepare to manage future outbreaks. This system poses a new diagnostic model for Monkeypox and other zoonotic diseases by incorporating these complex computational tools into the present healthcare systems. This advancement not only strengthens the diagnostic arsenal of zoonotic diseases but also expands the possibilities for the interception and prevention of such diseases in the future at the world level.
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References
[1] J. Otte and U. Pica-Ciamarra, “Emerging infectious zoonotic diseases: The neglected role of food animals,” One Health, vol. 13, p. 100323, 2021.
[2] M. Shafaati and M. Zandi, “State-of-the-art on monkey-pox virus: an emerging zoonotic disease,” Infection, vol. 50, no. 6, pp. 1425–1430, 2022.
[3] K. Chadaga et al., “Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review,” Di-agnostics, vol. 13, no. 5, p. 824, 2023.
[4] S. O. Akinola, Q.-G. Wang, P. Olukanmi, and T. Mar-wala, “Early Prediction of Monkeypox Virus Outbreak Using Machine Learning,” IETI Transactions on Data Analysis and Forecasting, vol. 1, no. 2, pp. 14–29, 2023.
[5] E. Alakunle, U. Moens, G. Nchinda, and M. I. Okeke, “Monkeypox virus in Nigeria: Infection biology, epi-demiology, and evolution,” Viruses, vol. 12, no. 11, p. 1257, 2020.
[6] D. A. Thal and T. C. Mettenleiter, “One Health—Key to Adequate Intervention Measures against Zoonotic Risks,” Pathogens, vol. 12, no. 3, p. 415, 2023.
[7] N. Kobayashi, “Impact of Emerging, Re-Emerging and Zoonotic Viral Infectious Diseases, in a Virologist’s Perspective,” Open Virology Journal, vol. 12, no. 1, pp. 131–133, 2018.
[8] S. Wongvibulsin and A. S. Adamson, “Deep learning for Mpox: Advances, challenges, and opportunities,” Med, vol. 4, no. 5, pp. 283–284, 2023.
[9] N. Haider et al., “COVID-19—Zoonosis or Emerging Infectious Disease?,” Frontiers in Public Health, vol. 8, p. 596944, 2020.
[10] D. S. Khafaga et al., “An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Net-work for Classifying Monkeypox Disease,” Diagnostics, vol. 12, no. 11, p. 2892, 2022.
[11] M. Shafaati and M. Zandi, “State-of-the-art on monkey-pox virus: an emerging zoonotic disease,” Infection, vol. 50, no. 6, pp. 1425–1430, 2022.
[12] D. S. Khafaga et al., “An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Net-work for Classifying Monkeypox Disease,” Diagnostics, vol. 12, no. 11, p. 2892, 2022.
[13] A. A. Abdelhamid et al., “Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm,” Mathematics, vol. 10, no. 19, p. 3614, 2022.
[14] M. M. Eid et al., “Meta-Heuristic Optimization of LSTM-Based Deep Network for Boosting the Predic-tion of Monkeypox Cases,” Mathematics, vol. 10, no. 20, p. 3845, 2022.
[15] D. S. Khafaga et al., “An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Net-work for Classifying Monkeypox Disease,” Diagnostics, vol. 12, no. 11, p. 2892, 2022.
[16] A. H. Alharbi et al., “Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm,” Biomimetics, vol. 8, no. 3, p. 313, 2023.
[17] A. H. Rabie and A. I. Saleh, “Monkeypox diagnosis using ensemble classification,” Artificial Intelligence in Medicine, vol. 143, p. 102618, 2023.
[18] Z. Sabir, S. Ben Said, and J. L. G. Guirao, “A Radial Ba-sis Scale Conjugate Gradient Deep Neural Network for the Monkeypox Transmission System,” Mathematics, vol. 11, no. 4, p. 975, 2023.
[19] S. A. Almutairi, “DL-MDF-OH2: Optimized Deep Learning-Based Monkeypox Diagnostic Framework Us-ing the Metaheuristic Harris Hawks Optimizer Algo-rithm,” Electronics, vol. 11, no. 24, p. 4077, 2022.
[20] A. K. Mandal, P. K. D. Sarma, and S. Dehuri, “Machine Learning Approaches and Particle Swarm Optimization Based Clustering for the Human Monkeypox Viruses: A Study,” Communications in Computer and Information Science, vol. 1737, pp. 313–332, 2022.
[21] T. Y. Taha et al., “Design and Optimization of a Mon-keypox Virus Specific Serological Assay,” Pathogens, vol. 12, no. 3, p. 396, 2023.
[22] A. Alshehri and S. Ullah, “Optimal control analysis of Monkeypox disease with the impact of environmental transmission,” AIMS Mathematics, vol. 8, no. 7, pp. 16926–16960, 2023.
[23] Samreen, S. Ullah, R. Nawaz, and A. Alshehri, “Math-ematical modeling of monkeypox infection with opti-mized preventive control analysis: A case study with 2022 outbreak,” The European Physical Journal Plus, vol. 138, no. 8, pp. 1–34, 2023.
[24] N. Dahiya et al., “Hyper-parameter tuned deep learn-ing approach for effective human monkeypox disease detection,” Scientific Reports, vol. 13, p. 15930, 2023.
[25] O. J. Peter, A. Abidemi, M. M. Ojo, and T. A. Ayoola, “Mathematical model and analysis of monkeypox with control strategies,” The European Physical Journal Plus, vol. 138, no. 3, pp. 1–20, 2023.
[26] E. H. I. Eliwa, A. M. El Koshiry, T. Abd El-Hafeez, and H. M. Farghaly, “Utilizing convolutional neural networks to classify monkeypox skin lesions,” Scientific Reports, vol. 13, p. 14495, 2023.
[27] K. Trang, A. H. Nguyen, N. G. M. Thao, B. Q. Vuong, and L. Ton-That, “Performance Enhancement in Pre-Trained Deep Learning Models for Monkeypox Skin Lesions Identification Using Feature Selection Algo-rithms,” in Proceedings of the 62nd Annual Conference of the Society of Instrument and Control Engineers, pp. 56–61, 2023.
[28] A. I. Saleh and A. H. Rabie, “Human monkeypox diag-nose (HMD) strategy based on data mining and artifi-cial intelligence techniques,” Computers in Biology and Medicine, vol. 152, p. 106383, 2023.
[29] S. T. Akinyemi et al., “A tale of two countries: Optimal control and cost-effectiveness analysis of monkeypox disease in Germany and Nigeria,” Healthcare Analytics, vol. 4, p. 100258, 2023.
[30] D. Nörz et al., “Rapid Adaptation of Established High-Throughput Molecular Testing Infrastructure for Mon-keypox Virus Detection,” Emerging Infectious Diseases, vol. 28, no. 9, p. 1765, 2022.
[31] A. Alshehri and S. Ullah, “Mathematical analysis of monkeypox infection with optimal control analysis: A case study with a new outbreak in the United States,” Mathematical Methods in the Applied Sciences, Early View, 2023.
[32] D. Bala et al., “MonkeyNet: A robust deep convolu-tional neural network for monkeypox disease detec-tion and classification,” Neural Networks, vol. 161, pp. 757–775, 2023.
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