APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR PREDICTING HYPERTENSION STATUS AND INDICATORS IN HADEJIA METROPOLITAN
Abstract
The responsibilities of hypertension or long blood pressure is rapidly increasing worldwide. Jigawa state in Nigeria seems to be one of the most affected states in the country. The frequency of hypertension in Hadejia forms an ongoing section of the overall responsibility in Jigawa state because of its population among local governments in the state. The purpose of this research is to determine the frequency and classification of a case of hypertension in Hadejia. A survey for some factors was conducted to identify which among the factors impact the prevalence of long blood pressure in Hadejia. It can be seen that the overall prevalence of hypertension in the study was found to be 45.97156% and 54.02844% were found to be non-hypertensive among the categories, those who are married have a higher prevalence of 35.07109%. The study produced the results shown in Table 2, which show the frequency of hypertensive and non-hypertensive patients among the categories and the prevalence of hypertension among those categories. Non-diabetic and those whose parents are hypertensive have the same prevalence of 34.12322% whereas those at or below 25 years of age have a less prevalence of 1.421801% of hypertension. Likewise, in Table 4, ANN with 64.3% of accuracy (sensitivity). The outcome for the testing sample performed better with an accuracy of 64.35% than that for the training sample with an accuracy of 70.4%, and the result shows that Age, Diabetics, and parental Hypertension Status are contributing to the prevalence of Hypertension or long blood pressure.
References
Adeloye, D., Basquill, C., Aderemi, A. V., Thompson, J. Y., & Obi, F. A. (2015). An estimate of the prevalence of hypertension in Nigeria: A systematic review and meta-analysis. Journal of Hypertension, 33(2), 230–242. https://doi.org/10.1097/HJH.0000000000000413 DOI: https://doi.org/10.1097/HJH.0000000000000413
Akinlua, J. T., Meakin, R., Umar, A. M., & Freemantle, N. (2015). Current prevalence pattern of hypertension in Nigeria: A systematic review. PLoS ONE, 10(10). https://doi.org/10.1371/journal.pone.0140021 DOI: https://doi.org/10.1371/journal.pone.0140021
Akwensi, P. H., Brantson, E. T., Niipele, J. N., & Ziggah, Y. Y. (2021). Performance evaluation of artificial neural networks for natural terrain classification. Applied Geomatics, 13(3), 453–465. https://doi.org/10.1007/s12518-021-00360-9 DOI: https://doi.org/10.1007/s12518-021-00360-9
Asemu, M. M., Yalew, A. W., Kabeta, N. D., & Mekonnen, D. (2021). Prevalence and risk factors of hypertension among adults: A community-based study in Addis Ababa, Ethiopia. PLoS ONE, 16(4 April). https://doi.org/10.1371/journal.pone.0248934 DOI: https://doi.org/10.1371/journal.pone.0248934
Bekesiene, S., Smaliukiene, R., & Vaicaitiene, R. (2021). Using artificial neural networks in predicting the level of stress among military conscripts. Mathematics, 9(6). https://doi.org/10.3390/math9060626 DOI: https://doi.org/10.3390/math9060626
Corporation, I. (1989). IBM SPSS Neural Networks 21. http://www.ibm.com/spss.
Forouzanfar, M. H., Liu, P., Roth, G. A., Ng, M., Biryukov, S., Marczak, L., Alexander, L., Estep, K., Abate, K. H., Akinyemiju, T. F., Ali, R., Alvis-Guzman, N., Azzopardi, P., Banerjee, A., Bärnighausen, T., Basu, A., Bekele, T., Bennett, D. A., Biadgilign, S., … Murray, C. J. L. (2017). Global burden of hypertension and systolic blood pressure of at least 110 to 115mmHg, 1990-2015. JAMA - Journal of the American Medical Association, 317(2), 165–182. https://doi.org/10.1001/jama.2016.19043 DOI: https://doi.org/10.1001/jama.2016.19043
Goit, L. N., & Yang, S. (2019). Treatment of Hypertension: A Review. Yangtze Medicine, 03(02), 101–123. https://doi.org/10.4236/ym.2019.32011 DOI: https://doi.org/10.4236/ym.2019.32011
Heart Association, A. (2021). What is High Blood Pressure?
Joshi, M. D., Ayah, R., Njau, E. K., Wanjiru, R., Kayima, J. K., Njeru, E. K., & Mutai, K. K. (2014). Prevalence of hypertension and associated cardiovascular risk factors in an urban slum in Nairobi, Kenya: A population-based survey. In BMC Public Health (Vol. 14, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/1471-2458-14-1177 DOI: https://doi.org/10.1186/1471-2458-14-1177
Kayima, J., Nankabirwa, J., Sinabulya, I., Nakibuuka, J., Zhu, X., Rahman, M., Longenecker, C. T., Katamba, A., Mayanja-Kizza, H., & Kamya, M. R. (2015). Determinants of hypertension in a young adult Ugandan population in epidemiological transition - The MEPI-CVD survey. BMC Public Health, 15(1). https://doi.org/10.1186/s12889-015-2146-y DOI: https://doi.org/10.1186/s12889-015-2146-y
López-Martínez, F., Núñez-Valdez, E. R., Crespo, R. G., & García-Díaz, V. (2020). An artificial neural network approach for predicting hypertension using NHANES data. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-67640-z DOI: https://doi.org/10.1038/s41598-020-67640-z
Mills, K. T., Stefanescu, A., & He, J. (2020). The global epidemiology of hypertension. In Nature Reviews Nephrology (Vol. 16, Issue 4, pp. 223–237). Nature Research. https://doi.org/10.1038/s41581-019-0244-2 DOI: https://doi.org/10.1038/s41581-019-0244-2
Nelson I, O. (2021). Management of hypertension in Nigeria: The barriers and challenges. Journal of Cardiology and Cardiovascular Medicine, 6(1), 023–025. https://doi.org/10.29328/journal.jccm.1001112 DOI: https://doi.org/10.29328/journal.jccm.1001112
Schutte, A. E., Srinivasapura Venkateshmurthy, N., Mohan, S., & Prabhakaran, D. (2021). Hypertension in Low- And Middle-Income Countries. Circulation Research, 808–826. https://doi.org/10.1161/CIRCRESAHA.120.318729 DOI: https://doi.org/10.1161/CIRCRESAHA.120.318729
Singh, S., Shankar, R., & Singh, G. P. (2017a). Prevalence and Associated Risk Factors of Hypertension: A Cross-Sectional Study in Urban Varanasi. International Journal of Hypertension, 2017. https://doi.org/10.1155/2017/5491838
Singh, S., Shankar, R., & Singh, G. P. (2017b). Prevalence and Associated Risk Factors of Hypertension: A Cross-Sectional Study in Urban Varanasi. International Journal of Hypertension, 2017. https://doi.org/10.1155/2017/5491838 DOI: https://doi.org/10.1155/2017/5491838
Siziya, S. (2012). Prevalence and Correlates of Hypertension among Adults Aged 25 Years or Older in a Mining Town of Kitwe, Zambia. Journal of Hypertension: Open Access, 01(03). https://doi.org/10.4172/2167-1095.1000105 DOI: https://doi.org/10.4172/2167-1095.1000105
Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., Ramirez, A., Schlaich, M., Stergiou, G. S., Tomaszewski, M., Wainford, R. D., Williams, B., & Schutte, A. E. (2020). 2020 International Society of Hypertension Global Hypertension Practice Guidelines. Hypertension, 75(6), 1334–1357. https://doi.org/10.1161/HYPERTENSIONAHA.120.15026 DOI: https://doi.org/10.1161/HYPERTENSIONAHA.120.15026
Usman, U., Arkilla, B. M., Ismail, A. A., & Dauda, U. (2022). Time-To-Optimal Control of Hypertension Using Kaplan-Meier Estimator, Cox Proportional Hazard and Weibull Model. Fudma Journal of Sciences, 6(5), 71–75. https://doi.org/10.33003/fjs-2022-0605-1096 DOI: https://doi.org/10.33003/fjs-2022-0605-1096
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)00044-7 DOI: https://doi.org/10.1016/S0169-2070(97)00044-7
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