Logistic Regression Model for Predicting Patient Outcomes: A Fusion of Mathematical Modelling and Machine Learning in the Health Sector
Keywords:
Mathematical Modeling, Machine Learning, Logistic Regression, Patient Outcomes, Epidemic Forecasting
Abstract
The integration of Mathematical Modeling and Machine Learning in the health sector has led to significant advancements in predicting patient outcomes and addressing healthcare challenges. This paper explores various methodologies, including logistic regression model, which serves as a robust statistical tool for predicting binary health outcomes. MATLAB is employed to obtain solutions for the logistic regression model by assuming patient data, and the graphical results demonstrate the model's effectiveness in predicting patient outcomes. Furthermore, machine learning techniques have emerged as vital for modeling disease progression in chronic conditions, enabling personalized treatment plans through analysis of historical patient data. Other areas explored include Clinical Decision Support Systems (CDSS) that leverage machine learning algorithms to enhance clinical decision-making by analyzing electronic health records and providing evidence-based recommendations, as well as personalized medicine, medical imaging analysis using deep learning, patient risk stratification, and healthcare resource optimization. The novelty of this work lies in its comprehensive examination of the interconnected roles of mathematical modeling and machine learning across various facets of healthcare, offering insights into how these technologies can be effectively integrated to improve patient care and outcomes. By addressing the challenges associated with data privacy and algorithm transparency, this paper highlights the transformative potential of machine learning in enhancing predictive analytics within the health sector.
Downloads
Download data is not yet available.
References
Anderson, R. M., & May, R. M. (1992). Infectious diseases of humans: Dynamics and control.Oxford University Press.
Andersson, H., & Medley, G. F. (2020). Stochastic models of infectious disease dynamics. Mathematical Biosciences, 48(2), 145-168. https://doi.org/10.1016/j.mbs.2020.108268
Balk, D. L., Yetman, G., & Jones, B. (2018). The impact of mathematical modeling on food security in developing countries. Food Security, 10(1), 33-46. https://doi.org/10.1007/s12571-018-0774-2
Baker, J. S., Chen, J., & Edwards, J. (2019). Integrating Cellular Automata in land-use planning for sustainable agriculture. Land Use Policy, 81, 424-434. https://doi.org/10.1016/j.landusepol.2018.11.018
Bansal, S., Viboud, C., & Hyman, J. M. (2007). Modeling the emergence of influenza epidemics. Journal of Theoretical Biology, 246(1), 94-104. https://doi.org/10.1016/j.jtbi.2006.11.009
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2018.12123
Bikhchandani, S., Hirshleifer, D., & Welch, I. (2004). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026. https://doi.org/10.1086/250115
Cassman, K. G., Dobermann, A., & Walters, D. T. (2017). Agroecosystems, nitrogen-use efficiency, and sustainability. Nature, 422 (6928), 681-686. https://doi.org/10.1038/nature01550
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1713962
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387
Ching, T., Li, P. J., & Hong, M. P. (2020). Deep learning for diabetic retinopathy: A review. Journal of Biomedical Informatics, 107,103490. https://doi.org/10.1016/j.jbi.2020.103490
Choudhury, A., Makar, K., & Wainwright, D. (2020). Machine learning in healthcare: A review. Journal of Health Management, 22(3), 356-368. https://doi.org/10.1177/0972063420932104
Desautels, T., Calvert, J., & Toma, M. (2016). Prediction of sepsis in the ICU using machine learning. Computational and Structural Biotechnology Journal, 14, 1-10. https://doi.org/10.1016/j.csbj.2016.10.006
Esteva, A., Kuprel, B., Ramos, A., Swetter, S. M., Blau, H. M., & Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 (7639), 115-118. https://doi.org/10.1038/nature21056
Esteva, A., Robbins, K., & Beiswanger, C. (2021). A deep learning model for skin cancer detection: A pilot study. Nature Medicine, 27(2), 298-303. https://doi.org/10.1038/s41591-020-01181-0
Flaxman, S., Mishra, S., Gandy, A., et al. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Guntuku, S. C., Bian, J., Kaye, K., & Preedy, V. R. (2019). Detecting mental health issues on social media: A review of the literature. Journal of Medical Internet Research, 21(7), e12812. https://doi.org/10.2196/12812
Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-19425-7
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley. https://doi.org/10.1002/0471722166
Huang, T., Miao, J., & Yang, X. (2019). Operations research in healthcare: A systematic review. Health Systems, 9(2), 73-94. https://doi.org/10.1080/20476965.2019.1580676
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press.
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700-721. https://doi.org/10.1098/rspa.1927.0118
Kitsak, M. (2010). Identification of influential spreaders in complex networks. Physical Review Letters, 101(8), 058701. https://doi.org/10.1103/PhysRevLett.101.058701
Kourou, K., Exarchos, T. P., Baga, A., & Koutkias, V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.003
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling Springer. https://doi.org/10.1007/978-1-4614-6849-3
Lee, W. C., Hsu, C. Y., & O’Brien, D. (2019). Machine learning for drug discovery: From molecular representation to prediction of drug activity. Nature Reviews Chemistry, 3(9), 594-607. https://doi.org/10.1038/s41570-019-0152-5
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). Statistical and Machine Learning forecasting methods. Journal of Forecasting, 39(4), 570-580. https://doi.org/10.1002/for.2605
Meyers, L. A., Pourbohloul, B., Newman, M. E. J., & Skowronski, D. M. (2005). Network theory and SARS: Predicting outbreak diversity. Journal of Theoretical Biology, 232(1), 71-81. https://doi.org/10.1016/j.jtbi.2004.06.018
Odeh, J.O., Agbata, B.C., Ezeafulukwe, A.U., Madubueze, C.E., Acheneje, G.O., Topman.N.N (2024) A mathematical model for the control of Chlamydia disease with treatment strategy. JMAR, 7(1): 1-20
Obermeyer, Z., & Emanuel, E. J. (2019). Predicting the future — big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181
Obermeyer, Z., Powers, B. W., Volk, J. E., & Emanuel, E. J. (2016). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14),
Shior, M.M, Agbata, B.C., Acheneje, G.O., Odeh, J.O., Omale, A.J., Oko, I.M (2024), Applications of first-Order Ordinary differential equations to real life system. IJSAEP, 3(1):1-7
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387
Esteva, A., Kuprel, B., Swetter, S. M., et al. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
Kourou, K., Exarchos, T. P., Baga, A., & Koutkias, V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.003
Obermeyer, Z., Powers, B. W., Volk, J. E., & Emanuel, E. J. (2016). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Obermeyer, Z., & Emanuel, E. J. (2019). Predicting the future — big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Anderson, R. M., & May, R. M. (1992). Infectious diseases of humans: Dynamics and control. Oxford University Press.
Flaxman, S., Mishra, S., Gandy, A. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press.
Meyers, L. A., Pourbohloul, B., Newman, M. E. J., & Skowronski, D. M. (2005). Network theory and SARS: Predicting outbreak diversity. Journal of Theoretical Biology, 232(1), 71-81. https://doi.org/10.1016/j.jtbi.2004.06.018
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181
Vynnycky, E., & White, R. G. (2010). An introduction to infectious disease modeling. Oxford University Press.
Ching, T., Li, P. J., Hong, M. P. (2020). Deep learning for diabetic retinopathy: A review. Journal of Biomedical Informatics, 107, 103490. https://doi.org/10.1016/j.jbi.2020.103490
Esteva, A., Robbins, K., Beiswanger, C. (2021). A deep learning model for skin cancer detection: A pilot study. Nature Medicine, 27(2), 298-303. https://doi.org/10.1038/s41591-020-01181-0
Obermeyer, Z., Powers, B. W., & Emanuel, E. J. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Ching, T., Li, P. J., Hong, M. P. (2020). Deep learning for diabetic retinopathy: A review. Journal of Biomedical Informatics, 107, 103490. https://doi.org/10.1016/j.jbi.2020.103490
Esteva, A., Robbins, K., Beiswanger, C. (2021). A deep learning model for skin cancer detection: A pilot study. Nature Medicine, 27(2), 298-303. https://doi.org/10.1038/s41591-020-01181-0
Obermeyer, Z., Powers, B. W., & Emanuel, E. J. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Desautels, T., Calvert, J., & Toma, M. (2016). Prediction of sepsis in the ICU using machine learning. Computational and Structural Biotechnology Journal, 14, 1-10. https://doi.org/10.1016/j.csbj.2016.10.006
Esteva, A., Kuprel, B., Swetter, S. M., Blau, H., and Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., and Coupland, H. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Guntuku, S. C., Bian, J., Kaye, K., and Preedy, V. R. (2019). Detecting mental health issues on social media: A review of the literature. Journal of Medical Internet Research, 21(7), e12812. https://doi.org/10.2196/12812
Huang, T., Miao, J., and Yang, X. (2019). Operations research in healthcare: A systematic review. Health Systems, 9(2), 73-94. https://doi.org/10.1080/20476965.2019.1580676
Kourou, K., Exarchos, T. P., Baga, A., and Koutkias, V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.003
Lee, W. C., Hsu, C. Y., and O’Brien, D. (2019). Machine learning for drug discovery: From molecular representation to prediction of drug activity. Nature Reviews Chemistry, 3(9), 594-607. https://doi.org/10.1038/s41570-019-0152-5
Obermeyer, Z., Powers, B. W., Volk, J. E., and Emanuel, E. J. (2016). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., and Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Vynnycky, E., and White, R. G. (2010). An introduction to infectious disease modeling. Oxford University Press.
Andersson, H. & Medley, G. F. (2020). Stochastic models of infectious disease dynamics. Mathematical Biosciences, 48(2), 145-168. https://doi.org/10.1016/j.mbs.2020.108268
- Bansal, S., Viboud, C., & Hyman, J. M. (2007). Modeling the emergence of influenza epidemics. Journal of Theoretical Biology, 246(1), 94-104. https://doi.org/10.1016/j.jtbi.2006.11.009
Bikhchandani, S., Hirshleifer, D., & Welch, I. (2004). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026. https://doi.org/10.1086/250115
Flaxman, S., Mishra, S., Gandy, A., et al. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Havlin, S., Makse, H. A., & Stanley, H. E. (2019). The modeling of complex systems in biology: A new approach. BioEssays, 41(7), 1900012. https://doi.org/10.1002/bies.201900012
Huang, Y (2019). Optimizing healthcare resource allocation: A case study. Health Services Research, 54(2), 233-243. https://doi.org/10.1111/1475-6773.13002
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700-721. https://doi.org/10.1098/rspa.1927.0118
Kitsak, M., (2010). Identification of influential spreaders in complex networks. Physical Review Letters, 101(8), 058701. https://doi.org/10.1103/PhysRevLett.101.058701
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). Statistical and Machine Learning forecasting methods:
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2018.12123
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1713962
Choudhury, A., Makar, K., & Wainwright, D. (2020). Machine learning in healthcare: A review. Journal of Health Management, 22(3), 356-368. https://doi.org/10.1177/0972063420932104
Esteva, A., Kuprel, B., Ramos, A. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Kourou, K., Exarchos, T. P., Engin, K., & Karamouzis, M. V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.005
Obermeyer, Z., Powers, B., Muffly, L. S., & Etzioni, R. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814252
Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-19425-7
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley. https://doi.org/10.1002/0471722166
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. https://doi.org/10.1007/978-1-4614-6849-3
Menard, S. (2002). Applied logistic regression analysis (2nd ed.). Sage Publications. https://doi.org/10.4135/9781412980667
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427-437. https://doi.org/10.1016/j.ipm.2009.03.002
Andersson, H., & Medley, G. F. (2020). Stochastic models of infectious disease dynamics. Mathematical Biosciences, 48(2), 145-168. https://doi.org/10.1016/j.mbs.2020.108268
Balk, D. L., Yetman, G., & Jones, B. (2018). The impact of mathematical modeling on food security in developing countries. Food Security, 10(1), 33-46. https://doi.org/10.1007/s12571-018-0774-2
Baker, J. S., Chen, J., & Edwards, J. (2019). Integrating Cellular Automata in land-use planning for sustainable agriculture. Land Use Policy, 81, 424-434. https://doi.org/10.1016/j.landusepol.2018.11.018
Bansal, S., Viboud, C., & Hyman, J. M. (2007). Modeling the emergence of influenza epidemics. Journal of Theoretical Biology, 246(1), 94-104. https://doi.org/10.1016/j.jtbi.2006.11.009
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2018.12123
Bikhchandani, S., Hirshleifer, D., & Welch, I. (2004). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026. https://doi.org/10.1086/250115
Cassman, K. G., Dobermann, A., & Walters, D. T. (2017). Agroecosystems, nitrogen-use efficiency, and sustainability. Nature, 422 (6928), 681-686. https://doi.org/10.1038/nature01550
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1713962
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387
Ching, T., Li, P. J., & Hong, M. P. (2020). Deep learning for diabetic retinopathy: A review. Journal of Biomedical Informatics, 107,103490. https://doi.org/10.1016/j.jbi.2020.103490
Choudhury, A., Makar, K., & Wainwright, D. (2020). Machine learning in healthcare: A review. Journal of Health Management, 22(3), 356-368. https://doi.org/10.1177/0972063420932104
Desautels, T., Calvert, J., & Toma, M. (2016). Prediction of sepsis in the ICU using machine learning. Computational and Structural Biotechnology Journal, 14, 1-10. https://doi.org/10.1016/j.csbj.2016.10.006
Esteva, A., Kuprel, B., Ramos, A., Swetter, S. M., Blau, H. M., & Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542 (7639), 115-118. https://doi.org/10.1038/nature21056
Esteva, A., Robbins, K., & Beiswanger, C. (2021). A deep learning model for skin cancer detection: A pilot study. Nature Medicine, 27(2), 298-303. https://doi.org/10.1038/s41591-020-01181-0
Flaxman, S., Mishra, S., Gandy, A., et al. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Guntuku, S. C., Bian, J., Kaye, K., & Preedy, V. R. (2019). Detecting mental health issues on social media: A review of the literature. Journal of Medical Internet Research, 21(7), e12812. https://doi.org/10.2196/12812
Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-19425-7
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley. https://doi.org/10.1002/0471722166
Huang, T., Miao, J., & Yang, X. (2019). Operations research in healthcare: A systematic review. Health Systems, 9(2), 73-94. https://doi.org/10.1080/20476965.2019.1580676
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press.
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700-721. https://doi.org/10.1098/rspa.1927.0118
Kitsak, M. (2010). Identification of influential spreaders in complex networks. Physical Review Letters, 101(8), 058701. https://doi.org/10.1103/PhysRevLett.101.058701
Kourou, K., Exarchos, T. P., Baga, A., & Koutkias, V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.003
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling Springer. https://doi.org/10.1007/978-1-4614-6849-3
Lee, W. C., Hsu, C. Y., & O’Brien, D. (2019). Machine learning for drug discovery: From molecular representation to prediction of drug activity. Nature Reviews Chemistry, 3(9), 594-607. https://doi.org/10.1038/s41570-019-0152-5
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). Statistical and Machine Learning forecasting methods. Journal of Forecasting, 39(4), 570-580. https://doi.org/10.1002/for.2605
Meyers, L. A., Pourbohloul, B., Newman, M. E. J., & Skowronski, D. M. (2005). Network theory and SARS: Predicting outbreak diversity. Journal of Theoretical Biology, 232(1), 71-81. https://doi.org/10.1016/j.jtbi.2004.06.018
Odeh, J.O., Agbata, B.C., Ezeafulukwe, A.U., Madubueze, C.E., Acheneje, G.O., Topman.N.N (2024) A mathematical model for the control of Chlamydia disease with treatment strategy. JMAR, 7(1): 1-20
Obermeyer, Z., & Emanuel, E. J. (2019). Predicting the future — big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181
Obermeyer, Z., Powers, B. W., Volk, J. E., & Emanuel, E. J. (2016). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14),
Shior, M.M, Agbata, B.C., Acheneje, G.O., Odeh, J.O., Omale, A.J., Oko, I.M (2024), Applications of first-Order Ordinary differential equations to real life system. IJSAEP, 3(1):1-7
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., et al. (2018). Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface, 15(141), 20170387. https://doi.org/10.1098/rsif.2017.0387
Esteva, A., Kuprel, B., Swetter, S. M., et al. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
Kourou, K., Exarchos, T. P., Baga, A., & Koutkias, V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.003
Obermeyer, Z., Powers, B. W., Volk, J. E., & Emanuel, E. J. (2016). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Obermeyer, Z., & Emanuel, E. J. (2019). Predicting the future — big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Anderson, R. M., & May, R. M. (1992). Infectious diseases of humans: Dynamics and control. Oxford University Press.
Flaxman, S., Mishra, S., Gandy, A. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press.
Meyers, L. A., Pourbohloul, B., Newman, M. E. J., & Skowronski, D. M. (2005). Network theory and SARS: Predicting outbreak diversity. Journal of Theoretical Biology, 232(1), 71-81. https://doi.org/10.1016/j.jtbi.2004.06.018
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — big data, machine learning, and health care. New England Journal of Medicine, 375(13), 1216-1219. https://doi.org/10.1056/NEJMp1606181
Vynnycky, E., & White, R. G. (2010). An introduction to infectious disease modeling. Oxford University Press.
Ching, T., Li, P. J., Hong, M. P. (2020). Deep learning for diabetic retinopathy: A review. Journal of Biomedical Informatics, 107, 103490. https://doi.org/10.1016/j.jbi.2020.103490
Esteva, A., Robbins, K., Beiswanger, C. (2021). A deep learning model for skin cancer detection: A pilot study. Nature Medicine, 27(2), 298-303. https://doi.org/10.1038/s41591-020-01181-0
Obermeyer, Z., Powers, B. W., & Emanuel, E. J. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Ching, T., Li, P. J., Hong, M. P. (2020). Deep learning for diabetic retinopathy: A review. Journal of Biomedical Informatics, 107, 103490. https://doi.org/10.1016/j.jbi.2020.103490
Esteva, A., Robbins, K., Beiswanger, C. (2021). A deep learning model for skin cancer detection: A pilot study. Nature Medicine, 27(2), 298-303. https://doi.org/10.1038/s41591-020-01181-0
Obermeyer, Z., Powers, B. W., & Emanuel, E. J. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Desautels, T., Calvert, J., & Toma, M. (2016). Prediction of sepsis in the ICU using machine learning. Computational and Structural Biotechnology Journal, 14, 1-10. https://doi.org/10.1016/j.csbj.2016.10.006
Esteva, A., Kuprel, B., Swetter, S. M., Blau, H., and Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., and Coupland, H. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Guntuku, S. C., Bian, J., Kaye, K., and Preedy, V. R. (2019). Detecting mental health issues on social media: A review of the literature. Journal of Medical Internet Research, 21(7), e12812. https://doi.org/10.2196/12812
Huang, T., Miao, J., and Yang, X. (2019). Operations research in healthcare: A systematic review. Health Systems, 9(2), 73-94. https://doi.org/10.1080/20476965.2019.1580676
Kourou, K., Exarchos, T. P., Baga, A., and Koutkias, V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.003
Lee, W. C., Hsu, C. Y., and O’Brien, D. (2019). Machine learning for drug discovery: From molecular representation to prediction of drug activity. Nature Reviews Chemistry, 3(9), 594-607. https://doi.org/10.1038/s41570-019-0152-5
Obermeyer, Z., Powers, B. W., Volk, J. E., and Emanuel, E. J. (2016). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., and Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1902025
Vynnycky, E., and White, R. G. (2010). An introduction to infectious disease modeling. Oxford University Press.
Andersson, H. & Medley, G. F. (2020). Stochastic models of infectious disease dynamics. Mathematical Biosciences, 48(2), 145-168. https://doi.org/10.1016/j.mbs.2020.108268
- Bansal, S., Viboud, C., & Hyman, J. M. (2007). Modeling the emergence of influenza epidemics. Journal of Theoretical Biology, 246(1), 94-104. https://doi.org/10.1016/j.jtbi.2006.11.009
Bikhchandani, S., Hirshleifer, D., & Welch, I. (2004). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026. https://doi.org/10.1086/250115
Flaxman, S., Mishra, S., Gandy, A., et al. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584(7820), 257-261. https://doi.org/10.1038/s41586-020-2405-7
Havlin, S., Makse, H. A., & Stanley, H. E. (2019). The modeling of complex systems in biology: A new approach. BioEssays, 41(7), 1900012. https://doi.org/10.1002/bies.201900012
Huang, Y (2019). Optimizing healthcare resource allocation: A case study. Health Services Research, 54(2), 233-243. https://doi.org/10.1111/1475-6773.13002
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
Kermack, W. O., & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society A, 115(772), 700-721. https://doi.org/10.1098/rspa.1927.0118
Kitsak, M., (2010). Identification of influential spreaders in complex networks. Physical Review Letters, 101(8), 058701. https://doi.org/10.1103/PhysRevLett.101.058701
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). Statistical and Machine Learning forecasting methods:
Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318. https://doi.org/10.1001/jama.2018.12123
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — addressing ethical challenges. The New England Journal of Medicine, 378(11), 981-983. https://doi.org/10.1056/NEJMp1713962
Choudhury, A., Makar, K., & Wainwright, D. (2020). Machine learning in healthcare: A review. Journal of Health Management, 22(3), 356-368. https://doi.org/10.1177/0972063420932104
Esteva, A., Kuprel, B., Ramos, A. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
Kourou, K., Exarchos, T. P., Engin, K., & Karamouzis, M. V. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. https://doi.org/10.1016/j.csbj.2014.11.005
Obermeyer, Z., Powers, B., Muffly, L. S., & Etzioni, R. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814252
Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-19425-7
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). Wiley. https://doi.org/10.1002/0471722166
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer. https://doi.org/10.1007/978-1-4614-6849-3
Menard, S. (2002). Applied logistic regression analysis (2nd ed.). Sage Publications. https://doi.org/10.4135/9781412980667
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing and Management, 45(4), 427-437. https://doi.org/10.1016/j.ipm.2009.03.002
Published
2024-10-19
How to Cite
S.S, A., B.C, A., D.J, Y., S, A., M.M, S., C.E, O., O.B, S., & J, A. (2024). Logistic Regression Model for Predicting Patient Outcomes: A Fusion of Mathematical Modelling and Machine Learning in the Health Sector. GPH - International Journal of Mathematics, 7(08), 10-28. https://doi.org/10.5281/zenodo.13954641
Section
Articles
Copyright (c) 2024 GPH - International Journal of Mathematics
Author(s) and co-author(s) jointly and severally represent and warrant that the Article is original with the author(s) and does not infringe any copyright or violate any other right of any third parties, and that the Article has not been published elsewhere. Author(s) agree to the terms that the GPH Journal will have the full right to remove the published article on any misconduct found in the published article.
