Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm

Idowu Peter Adebayo

doi:doi:10.11648/j.ajmcm.20170202.12

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Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm

Idowu Peter Adebayo

Published in American Journal of Mathematical and Computer Modelling (Volume 2, Issue 2)

Received: 8 December 2016 Accepted: 19 December 2016 Published: 24 February 2017

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Abstract

This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.

Published in	American Journal of Mathematical and Computer Modelling (Volume 2, Issue 2)
DOI	10.11648/j.ajmcm.20170202.12
Page(s)	48-59
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Hypertension Risk Factors, ID3, C4.5, Prediction, Classification, Decision Trees

References

[1]	Jing-Song, L., Hai-Yan, Y. and Xiao-Guang, Z. (2011). Data Mining in Hospital Information System. In New Fundamental Technologies in Data Mining, Prof. Kimito Funatsu (Ed.). ISBN: 978-953-307-547-1. Available from: http://www.intechopen.com/books/new-fundamental-technologies-in-data-mining/data-miningin-hospital-information-system.
[2]	Aqueel, A. and Shaikh, A. H. (2012). Data Mining Techniques to find out Heart Diseases: An Overview. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 1 (4): 1–6.
[3]	Chen, J., and Greiner, R. (1999). Comparing Bayesian Network Classifiers. In Proceedings of UAI-99: 101–108.
[4]	Agbelusi, O. (2014). Development of a predictive model for survival of HIV/AIDS patients in South-western Nigeria, Unpublished MPhil Thesis, Obafemi Awolowo University, Ile-Ife, Nigeria.
[5]	Idowu, P. A., Aladekomo, T. A., Williams, K. O. and Balogun, J. A. (2015). Predictive model for likelihood of Sickle cell aneamia (SCA) among pediatric patients using fuzzy logic. Transactions in networks and communications 31 (1): 31–44.
[6]	Waijee, A. K., Higgings, P. D. R. and Singal, A. G. (2013). A Primer on Predictive Models. Clinical and Translational Gastroenterology 4 (44): 1–4.
[7]	Lackland, D. T. and Weber, M. A. (2015). Global burden of cardiovascular disease and stroke: hypertension at the core. The Canadian journal of cardiology 31 (5): 569–71. doi:10.1016/j.cjca.2015.01.009.
[8]	Poulter, N. R., Prabhakaran, D. and Caulfield, M. (2015). Hypertension. Lancet 386(9995): 801–812. doi:10.1016/s0140-6736(14)61468-9.
[9]	[10Carretero, O. A. and Oparil, S. (2000). Essential hypertension, Part I: definition and etiology. Circulation 101 (3): 329–35. doi:10.1161/01.CIR.101.3.329.
[10]	Wokoma, F. S. and Alasia, D. D. (2011). Blood pressure pattern in Barako: a rural community in Rivers State, Nigeria. Niger Health J 11: 8-13.
[11]	World Health Organization, WHO (2011). WHO Maps: Non-communicable disease trend in all countries. World Health Global Report, World Health Organization.
[12]	Unachukwu, C. N., Agomuoh, D. I. and Alasia, D. D. (2008). Pattern of non-communicable diseases among medical admissions in Port Harcourt, Nigeria. Niger J Clin Pract 11: 14-17.
[13]	World Health Organization, WHO (2002). The World Health Report: 2002: Reducing the Risks, Promoting Healthy Life. World Health Organization, Geneva.
[14]	Omran, A. R. (1971). The epidemiologic transition: A theory of the epidemiology of population change. Milbank Mem Fund Q 49: 509-38.
[15]	Cressman, M. D. and Gifford, R. W. (1983). Hypertension and stroke. Journal of the American College of Cardiology 1: 521-527.
[16]	Post, W. S., Hill, M. N. and Dennison, J. L. (2003). High prevalence of target organ damage in young, African American Inner-city men with hypertension. Journal of clinical hypertension 5: 24-30.
[17]	Khakurel, S., Agrawal, R. K. and Hada, R. (2009). Pattern of end stage renal disease in a tertiary care center. Journal of the Nepal Medical Association 48: 126–130.
[18]	Cuspidi, C., Lonati, L., Sampieri, L., Michev, L., Macca, G., Rocanova, J. I., Salerno, M., Fusi, V., Leonetti, G. and Zanchetti, A. (2000). Prevalence of target organ damage in treated hypertensive patients: different impact of clinic and ambulatory blood pressure control. Journal of Hypertension 18: 803-809.
[19]	Neal, B., MacMahon, S. and Chapman, N. (2000). Effects of ACE inhibitors, calcium antagonists and other blood pressure lowering drugs: results of prospectively designed overviews of randomized controlled trials. Lancet 356: 1955-1964.
[20]	Nornchahal, K., Ashton, W. D. and Wood, D. A. (2000). Alcohol consumption, metabolic cardiovascular risk factors and hypertension in women. Int J Epdermiol 29: 57-64.
[21]	Laurenzi, M., Mancini, M., Menotti, A, Stamler, J., Stamler, R., Trevisan, M. and Zanchetti, A. (1990). Multiple risk factors in hypertension: results from the Gubbio study. J Hypertens 8 (Supplementary): 7-12.
[22]	Chukwuonye, I. I., Chuku, A., Onyeonoro, U. U., Madukwe, O. O., Oviasu, E. and Ogah, O. S. (2013). A rural and urban cross-sectional study on alcohol consumption among adult Nigerians in Abia state. Int J Med Biomed Res 2: 179-185.
[23]	Nwaneli, C. U. and Omejua, E. G. (2010). Clinical presentation and aetiology of hypertension in young adults in Nnewi South East Nigeria. Afrimed J 1: 24-26.
[24]	Oladapo, O. O., Salako, L., Sadiq, L., Shoyinka, K., Adedapo, K. and Falase, A. O. (2012). Target-organ damage and cardiovascular complications in hypertensive Nigerian Yoruba adults: a cross-sectional study. Cardiovasc J Afr 23: 379-384.
[25]	Diwe, K. C., Enwere, O. O., Uwakwe, K. A., Duru, C. B. and Chineke, H. N. (2015). Prevalence and awareness of hypertension and associated risk factors among bank workers in Owerri, Nigeria. Int J Med Biomed Res 4 (3): 142-148.
[26]	Cheng, T. H., Wei, C. P. and Tseng, V. S. (2006). Feature Selection for Medical Data Mining: Comparisons of Expert Judgment and Automatic Approaches. In Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).
[27]	Shillabeer, A. and Roddick, J (2007). Establishing a Lineage for Medical Knowledge Discovery. ACM International Conference Proceeding Series 311 (70): 29-37.
[28]	Cao, X., Maloney, K. B. and Brusic, V. (2008). Data mining of cancer vaccine trials: a bird's-eye view. Immunome Research 4:7 - 11. DOI:10.1186/1745-7580-4-7.
[29]	Mayilvaganan, M and Rajeswari, K. (2014). Human Blood Pressure Classification Analysis Using Fuzzy Logic Control System in Data Mining. International Journal of Emerging Trends and Technology in Computer Science (IJETICS) 3 (1): 305–306.
[30]	Waghmare, K. (2013). Prediction of Occurrence of Non-Communicable Diseases Depending on Lifestyle Habits Clustering Data Mining Technique. In Proceedings of International Conference on Emerging Research in Computing, Information, Communication and Applications ERCICA, 2013: 1–5.

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APA Style

Idowu Peter Adebayo. (2017). Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm. American Journal of Mathematical and Computer Modelling, 2(2), 48-59. https://doi.org/10.11648/j.ajmcm.20170202.12

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ACS Style

Idowu Peter Adebayo. Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm. Am. J. Math. Comput. Model. 2017, 2(2), 48-59. doi: 10.11648/j.ajmcm.20170202.12

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AMA Style

Idowu Peter Adebayo. Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm. Am J Math Comput Model. 2017;2(2):48-59. doi: 10.11648/j.ajmcm.20170202.12

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@article{10.11648/j.ajmcm.20170202.12,
  author = {Idowu Peter Adebayo},
  title = {Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {2},
  number = {2},
  pages = {48-59},
  doi = {10.11648/j.ajmcm.20170202.12},
  url = {https://doi.org/10.11648/j.ajmcm.20170202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20170202.12},
  abstract = {This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.},
 year = {2017}
}

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TY  - JOUR
T1  - Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm
AU  - Idowu Peter Adebayo
Y1  - 2017/02/24
PY  - 2017
N1  - https://doi.org/10.11648/j.ajmcm.20170202.12
DO  - 10.11648/j.ajmcm.20170202.12
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 48
EP  - 59
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20170202.12
AB  - This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.
VL  - 2
IS  - 2
ER  -

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Author Information

Idowu Peter Adebayo

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

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Plain Text BibTeX RIS

APA Style

Idowu Peter Adebayo. (2017). Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm. American Journal of Mathematical and Computer Modelling, 2(2), 48-59. https://doi.org/10.11648/j.ajmcm.20170202.12

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ACS Style

Idowu Peter Adebayo. Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm. Am. J. Math. Comput. Model. 2017, 2(2), 48-59. doi: 10.11648/j.ajmcm.20170202.12

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AMA Style

Idowu Peter Adebayo. Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm. Am J Math Comput Model. 2017;2(2):48-59. doi: 10.11648/j.ajmcm.20170202.12

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@article{10.11648/j.ajmcm.20170202.12,
  author = {Idowu Peter Adebayo},
  title = {Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm},
  journal = {American Journal of Mathematical and Computer Modelling},
  volume = {2},
  number = {2},
  pages = {48-59},
  doi = {10.11648/j.ajmcm.20170202.12},
  url = {https://doi.org/10.11648/j.ajmcm.20170202.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20170202.12},
  abstract = {This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Predictive Model for the Classification of Hypertension Risk Using Decision Trees Algorithm
AU  - Idowu Peter Adebayo
Y1  - 2017/02/24
PY  - 2017
N1  - https://doi.org/10.11648/j.ajmcm.20170202.12
DO  - 10.11648/j.ajmcm.20170202.12
T2  - American Journal of Mathematical and Computer Modelling
JF  - American Journal of Mathematical and Computer Modelling
JO  - American Journal of Mathematical and Computer Modelling
SP  - 48
EP  - 59
PB  - Science Publishing Group
SN  - 2578-8280
UR  - https://doi.org/10.11648/j.ajmcm.20170202.12
AB  - This study is focused with the development of a predictive model for the classification of the risk of hypertension among Nigerians using decision trees algorithms based on historical information elicited about the risk of hypertension among selected respondents in southwestern Nigeria. Following the identification of the risk factors of hypertension from experienced cardiologists, structured questionnaires were used to collect information about the risk factors and the associated risk of hypertension from selected respondents. The predictive model was formulated using two (2) decision trees algorithms, namely: C4.5 and ID3 based on the information collected. The predictive model was simulated using the Waikato Environment for Knowledge Analysis (WEKA) using the 10-fold cross validation technique for model training and testing. The results revealed that the decision trees algorithms selected some risk factors among those identified as most predictive for the risk of hypertension based on the information inferred from the dataset collected. The variables were used by the decision trees algorithm to deduce the decision trees that were used to infer the risk of hypertension based on the values of the identified risk factors. The ID3 with an accuracy of 100% outperformed the C4.5 which showed an accuracy of 86.36%. The variables identified by the algorithms can help assist cardiologists concentrate on a smaller yet important set of risk factors for identifying the risk of hypertension using rules derived from the path along the decision trees based on the value of the risk factors of the individual.
VL  - 2
IS  - 2
ER  -

Copy | Download