Predictive Modeling and Statistical Analysis of Hypertension Cases at a Small Teaching Hospital

References

[1] Allison, P. D. (2010). Survival analysis using SAS: A practical guide (2nd ed.). SAS Institute.

[2] Angstman, K. B., Maxson, J. A., & Cutshall, S. M. (2020). Limitations of hypertension care in community practice settings. BMC Health Services Research, 20, 176. https://doi.org/10.1186/s12913-020-4991-7

[3] Anyibama, B. J., Orjinta, K. K., Omisogbon, T. O., Atalor, S. I., Daniels, E. O., Fadipe, E. & Galadima, D. A. (2025). Modern Agricultural Technologies for Sustainable Food Production: A Comprehensive Review of Technological Innovations and Their Impact on Global Food Systems. International Journal of Innovative Science and Research Technology ISSN No:-2456-2165 Volume 10, Issue 2 https://doi.org/10.5281/zenodo.14964384

[4] Ataklte, F., Erqou, S., Kaptoge, S., Taye, B., Echouffo-Tcheugui, J. B., & Kengne, A. P. (2015). Burden of undiagnosed hypertension in sub-Saharan Africa: A systematic review and meta-analysis. Hypertension, 65(2), 291–298. https://doi.org/10.1161/HYPERTENSIONAHA.114.04394

[5] Azonuche, T. I., & Enyejo, J. O. (2025). Adaptive Risk Management in Agile Projects Using Predictive Analytics and Real-Time Velocity Data Visualization Dashboard. International Journal of Innovative Science and Research Technology Volume 10, Issue 4, April – 2025 ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/25apr2002

[6] Bakris, G. L., Williams, M., Dworkin, L., et al. (2021). Preserving renal function in adults with hypertension and diabetes. Hypertension, 77(3), 593–601. https://doi.org/10.1161/HYPERTENSIONAHA.120.16127

[7] 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.2017.18391

[8] Carey, R. M., Whelton, P. K., Aronow, W. S., et al. (2018). 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults. Journal of the American College of Cardiology, 71(19), e127–e248. https://doi.org/10.1016/j.jacc.2017.11.006

[9] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785

[10] Chobanian, A. V., Bakris, G. L., Black, H. R., et al. (2003). Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension, 42(6), 1206–1252. https://doi.org/10.1161/01.HYP.0000107251.49515.c2

[11] Ehret, G. B., Munroe, P. B., Rice, K. M., et al. (2011). Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature, 478(7367), 103–109. https://doi.org/10.1038/nature10405

[12] Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis (3rd ed.). CRC Press.

[13] Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed.). Springer.

[14] Ibrahim, M. M., & Damasceno, A. (2012). Hypertension in developing countries. The Lancet, 380(9841), 611–619. https://doi.org/10.1016/S0140-6736(12)60861-7

[15] Imoh, P. O., & Idoko, I. P. (2022). Gene-Environment Interactions and Epigenetic Regulation in Autism Etiology through Multi-Omics Integration and Computational Biology Approaches. International Journal of Scientific Research and Modern Technology, 1(8), 1–16. https://doi.org/10.38124/ijsrmt.v1i8.463

[16] Kearney, P. M., Whelton, M., Reynolds, K., et al. (2005). Global burden of hypertension: analysis of worldwide data. The Lancet, 365(9455), 217–223. https://doi.org/10.1016/S0140-6736(05)17741-1

[17] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

[18] Manolio, T. A., Collins, F. S., Cox, N. J., et al. (2012). Finding the missing heritability of complex diseases. Nature, 461(7265), 747–753. https://doi.org/10.1038/nature08494

[19] Miotto, R., Li, L., Kidd, B. A., & 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

[20] Mills, K. T., Stefanescu, A., & He, J. (2020). The global epidemiology of hypertension. Nature Reviews Nephrology, 16(4), 223–237. https://doi.org/10.1038/s41581-019-0244-2

[21] Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — Big data, machine learning, and clinical medicine. The New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181

[22] Oparil, S., Acelajado, M. C., Bakris, G. L., et al. (2018). Hypertension. Nature Reviews Disease Primers, 4, 18014. https://doi.org/10.1038/nrdp.2018.14

[23] Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259

[24] Rahman, M. M., Islam, M. T., & Amin, A. (2021). Machine learning-based predictive modeling for hypertension risk classification using health survey data. Informatics in Medicine Unlocked, 23, 100536. https://doi.org/10.1016/j.imu.2021.100536

[25] Sarki, A. M., Nduka, C. U., Stranges, S., & Kandala, N. B. (2015). Prevalence of hypertension in low- and middle-income countries: A systematic review and meta-analysis. Medicine, 94(50), e1959. https://doi.org/10.1097/MD.0000000000001959

[26] Twisk, J. W. R. (2013). Applied longitudinal data analysis for epidemiology: A practical guide (2nd ed.). Cambridge University Press.

[27] Wang, Y., Xie, T., Wei, Y., & Zhang, M. (2021). Predictive modeling of hypertension using machine learning: An application to a real-world dataset. Computer Methods and Programs in Biomedicine, 200, 105846. https://doi.org/10.1016/j.cmpb.2020.105846

[28] Whelton, P. K., Carey, R. M., Aronow, W. S., et al. (2018). 2017 ACC/AHA hypertension guideline. Journal of the American College of Cardiology, 71(19), e127–e248. https://doi.org/10.1016/j.jacc.2017.11.006

[29] Williams, B., Mancia, G., Spiering, W., et al. (2018). 2018 ESC/ESH guidelines for the management of arterial hypertension. European Heart Journal, 39(33), 3021–3104. https://doi.org/10.1093/eurheartj/ehy339

[30] Zhou, D., Xi, B., Zhao, M., Wang, L., Veeranki, S. P., & Zhang, M. (2021). Uncontrolled hypertension increases risk of all-cause and cardiovascular disease mortality in US adults: The NHANES study. Journal of Clinical Hypertension, 23(5), 961–969. https://doi.org/10.1111/jch.14233

@article{1708340,
      author = {Adama Gaye},
      title = {Predictive Modeling and Statistical Analysis of Hypertension Cases at a Small Teaching Hospital},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {630-640},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708340.pdf},
      abstract = {Hypertension remains a major public health concern, particularly in resource-constrained environments such as small teaching hospitals, where early diagnosis and effective management are critical yet often under-optimized. This study investigates the patterns and predictors of hypertension using statistical analysis and predictive modeling techniques to enhance clinical decision-making. Patient data were collected from a small teaching hospital over a defined period, focusing on demographic, lifestyle, and clinical variables. Descriptive statistics were employed to understand prevalence trends, while predictive models?such as logistic regression and decision trees?were developed to identify key risk factors and forecast the likelihood of hypertension occurrence. The models were evaluated using accuracy, sensitivity, specificity, and area under the ROC curve (AUC) to ensure reliability and applicability in real-world settings. Results revealed significant associations between hypertension and factors such as age, BMI, and family history. The predictive models demonstrated robust performance, offering potential integration into electronic health record systems for proactive screening. This study underscores the value of data-driven approaches in enhancing hypertension management, especially within the constraints of small hospital settings, and recommends further expansion of predictive tools for broader public health applications.},
      keywords = {Predictive Modeling, Statistical Analysis, Hypertension Cases, Small Teaching Hospital},
      month = {May},
  }