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), 2017. Published by Science Publishing Group

Keywords

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

References

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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