References
Aguh, P. S., Udu, C. E., Chukwumuanya, E. O. and Okpala, C. C. (2025). Machine Learning Applications for Production Scheduling Optimization. Journal of Exploratory Dynamic Problems, vol. 2, iss. 4, https://edp.web.id/index.php/edp/article/view/137 Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., and Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. https://doi.org/10.1377/hlthaff.2014.0041 Bandi, M., Vemula, R. and Vallu, S. (2024). Predictive Analytics in Healthcare: Enhancing Patient Outcomes through Data-Driven Forecasting and Decision-Making. International Numeric Journal of Machine Learning and Robots, 2024, https://injmr.com/index.php/fewfewf/article/view/144/37 Bardsley, M., Steventon, A., Smith, J., and Dixon, J. (2019). Evaluating integrated and community-based care: How do we know what works? The Health Foundation. Bauder, R. A., Khoshgoftaar, T. M., and Seliya, N. (2017). A survey on the utilization of healthcare data in healthcare fraud detection. Journal of Big Data, 4(1), 1-24. https://doi.org/10.1186/s40537-017-0076-6 Bruynseels, K., Santoni de Sio, F., and van den Hoven, J. (2018). Digital twins in health care: Ethical implications of an emerging engineering paradigm. Frontiers in Genetics, 9, 31. https://doi.org/10.3389/fgene.2018.00031 Chen, J. H., and Asch, S. M. (2017). Machine learning and prediction in medicine — beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507–2509. https://doi.org/10.1056/NEJMp1702071 Choudhury, A., and Asan, O. (2020). Role of artificial intelligence in patient safety outcomes: Systematic literature review. JMIR Medical Informatics, 8(7), e18599. https://doi.org/10.2196/18599 Collins, G. S., Reitsma, J. B., Altman, D. G., and Moons, K. G. (2015). Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1), 55–63. https://doi.org/10.7326/M14-0697 Collins, F. S., and Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372(9), 793–795. https://doi.org/10.1056/NEJMp1500523 Dorsey, E. R. and Topol, E. J. (2020). Telemedicine 2020 and the next decade. The Lancet, 395(10227), 859. https://doi.org/10.1016/S0140-6736(20)30424-4 Gates, J. D., Yulianti and Pangilinan, G. A. (2024). Big Data Analytics for Predictive Insights in Healthcare. International Transactions on Artificial Intelligence, vol. 3, iss. 1, file:///C:/Users/Admin/Downloads/_V3N1-006.pdf Gerke, S., Minssen, T., and Cohen, I. G. (2020). Ethical and legal challenges of artificial intelligence-driven healthcare. Artificial Intelligence in Healthcare, 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5 Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., and Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101 Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., and Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3 Henry, K. E., Hager, D. N., Pronovost, P. J., and Saria, S. (2015). A targeted real-time early warning score (TREWScore) for septic shock. Science Translational Medicine, 7(299), 299ra122. https://doi.org/10.1126/scitranslmed.aab3719 Huckvale, K., Venkatesh, S., and Christensen, H. (2019). Toward clinical digital phenotyping: A timely opportunity to consider purpose, quality, and safety. npj Digital Medicine, 2(1), 88. https://doi.org/10.1038/s41746-019-0166-1 Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., and Kripalani, S. (2011). Risk prediction models for hospital readmission: A systematic review. JAMA, 306(15), 1688–1698. https://doi.org/10.1001/jama.2011.1515 Kellermann, A. L., and Jones, S. S. (2013). What it will take to achieve the as-yet-unfulfilled promises of health information technology. Health Affairs, 32(1), 63-68. https://doi.org/10.1377/hlthaff.2012.0693 Khairat, S., Marc, D., Crosby, W., and Al Sanousi, A. (2018). Reasons for physicians not adopting clinical decision support systems: Critical analysis. JMIR Medical Informatics, 6(2), e24. https://doi.org/10.2196/medinform.8912 Kleinman, R. A., and Merkel, C. (2020). Digital contact tracing for COVID-19. JAMA, 324(10), 935–936. https://doi.org/10.1001/jama.2020.15226 Khoury, M. J., and Ioannidis, J. P. (2014). Big data meets public health. Science, 346(6213), 1054- https://doi.org/10.1126/science.aaa2709 Kumar, A., Shankar, R., and Thakur, L. S. (2013). A big data map reduce framework for supply chain analytics. Procedia-Social and Behavioral Sciences, 133, 370–377. https://doi.org/10.1016/j.sbspro.2014.04.204 Kumar, S., Nilsen, W. J., Abernethy, A., et al. (2013). Mobile health technology evaluation: the mHealth evidence workshop. American Journal of Preventive Medicine, 45(2), 228-236. https://doi.org/10.1016/j.amepre.2013.03.017 Kuo, A. M. H. (2011). Opportunities and challenges of cloud computing to improve health care services. Journal of Medical Internet Research, 13(3), e67. https://doi.org/10.2196/jmir.1867 Laney, D. (2001). 3D data management: Controlling data volume, velocity, and variety. Meta Group. Maharana, A., and Nsoesie, E. O. (2018). Use of big data in understanding public health outcomes in India. Global Health Action, 11(sup3), https://doi.org/10.1080/16549716.2018.1505577 Mandel, J. C., Kreda, D. A., Mandl, K. D., Kohane, I. S., and Ramoni, R. B. (2016). SMART on FHIR: A standards-based, interoperable apps platform for electronic health records. Journal of the American Medical Informatics Association, 23(5), 899–908. https://doi.org/10.1093/jamia/ocv189 Marr, B. (2016). Big data in practice: How 45 successful companies used big data analytics to deliver extraordinary results. Wiley. McGraw, D. (2013). Building public trust in uses of Health Insurance Portability and Accountability Act de-identified data. Journal of the American Medical Informatics Association, 20(1), 29–34. https://doi.org/10.1136/amiajnl-2012-000936 Miotto, R., Li, L., Kidd, B. A., and Dudley, J. T. (2016). Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094. https://doi.org/10.1038/srep26094 Nwamekwe, C. O. and Okpala, C. C. (2025a). Machine Learning-Augmented Digital Twin Systems for Predictive Maintenance in High-Speed Rail Networks. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, iss. 1, https://www.allmultidisciplinaryjournal.com/uploads/archives/ 20250212104201_MGE- 2025-1-306.1.pdf Nwamekwe, C. O., Ewuzie, N. V., Okpala, C. C., Ezeanyim, O. C., Nwabueze, C. V. and Nwabunwanne, E. C. (2025b). Optimizing Machine Learning Models for Soil Fertility Analysis: Insights from Feature Engineering and Data Localization. Gazi University Journal of Science, vol. 12, iss. 1, https://dergipark.org.tr/en/pub/gujsa/issue/90827/1605587 Nwamekwe, C. O., Okpala, C. C. and Okpala, S. C. (2024). Machine Learning-Based Prediction Algorithms for the Mitigation of Maternal and Fetal Mortality in the Nigerian Tertiary Hospitals. International Journal of Engineering Inventions, vol. 13, iss. 7, http://www.ijeijournal.com/papers/Vol13-Issue7/1307132138.pdf Nwankwo