Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine

Samson Alobalorun Bamidele; Adanze Asinobi; Ngozi Chidozie Egejuru; Peter Adebayo Idowu

doi:doi:10.11648/j.cbb.20200802.14

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Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine

Samson Alobalorun Bamidele, Adanze Asinobi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu

Published in Computational Biology and Bioinformatics (Volume 8, Issue 2)

Received: 30 May 2020 Accepted: 23 October 2020 Published: 4 November 2020

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Abstract

This study developed a model for the survival of diabetes mellitus patients in Nigeria. The study identified the variables monitored during the treatment of diabetes mellitus patients, formulated, and validated the predictive model for the survival time of diabetes mellitus patients. In order to achieve the aim of this study, structured interview with professional physicians so as to identify the variables for the survival time of diabetes mellitus with historical datasets were collected based on the variables monitored during treatment. The model was formulated using the support vector machine based on the variables identified and simulated using the WEKA Software using the historical datasets for training the model. The results showed that data collected from 29 patients at a hospital located in south-western Nigeria consisting of 32 attributes with a target class containing information about the survival time of each diabetes mellitus patient. The study concluded that the model can also be integrated into existing Health Information System (HIS) which captures and manages clinical information which can be fed to the predictive model thus improving the decisions affecting the patient’s outcome and the real-time assessment of clinical information affecting the patient’s survival of diabetes.

Published in	Computational Biology and Bioinformatics (Volume 8, Issue 2)
DOI	10.11648/j.cbb.20200802.14
Page(s)	52-61
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

Support Vector Machine, Diabetes Mellitus, Survival, Model, Predictive

References

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[2]	Aguocha, B. U., Ukpabi, J. O. and Onyeonoro, U. U. (2013). Pattern of diabetic mortality in a tertiary health facility in south-eastern Nigeria. African Journal of Diabetes Medicine 21: 14–16.
[3]	Chijioke, A., Adamu, A. N. and Makusidi, A. M. (2010). Mortality pattern among type 2 diabetes patients in Ilorin, Nigeria. JEMDSA 15 (2): 1-4.
[4]	Cox, David R (1972). "Regression Models and Life-Tables". Journal of the Royal Statistical Society, Series B. 34 (2): 187–220.
[5]	Cruz, J. A. and Wishart, D. S. (2006). Applications of Machine Learning in Cancer Prediction and Prognosis. Cancer Informatics 2: 59-75.
[6]	Idowu, P. A., Agbelusi, O. and Aladekomo, T. A. (2016). The Prediction of Pediatric HIV/AIDS Patients’ Survival: A Data Mining Approach. Asian Journal of Computer and Information Systems 4 (3): 87-94.
[7]	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.
[8]	International Diabetes Federation Editorial Team. Mortality (2003). In: Guariguata, L., Nolan, T., Beagley, J., Linnenkamp, U. and Jacqmain, O. (Eds.). Diabetes Atlas, 6th edition. Brussels: International Diabetes Federation (IDF): 49.
[9]	Kumari, V. A. and Chitra, R. (2013). Classification of Diabetes Disease Using Support Vector Machine. International Journal of Engineering Research and Application 3 (2): 1897-1801.
[10]	Li, J., Serpen, G., Selman, S., Franchetti, M., Riesen, M. and Schneider, C. (2010). Bayes Net Classifiers for Prediction of Renal Graft Status and Survival Period. World Academy of Science, Engineering and Technology 4 (3): 128-133.
[11]	Onen, C. (1998). Diabetes morbidity and mortality in Botswana: a retrospective analysis of hospital based data on diabetic patients, 1980–1994. International Diabetes Digest 13: 96–9.
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[14]	Waijee, A. K., Joyce, J. C. and Wang, S. J. (2010). Algorithms outperform metabolite tests in predicting response of patients with inflammatory bone disease to thiopurines. Clin Gastroenterol Hepatol +8: 143-150.
[15]	WHO (2020). Diabetes: Key Facts. Available from https://www.who.int/news-room/fact-sheets/detail/diabetes [Accessed may 29, 2020].

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APA Style

Samson Alobalorun Bamidele, Adanze Asinobi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu. (2020). Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine. Computational Biology and Bioinformatics, 8(2), 52-61. https://doi.org/10.11648/j.cbb.20200802.14

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ACS Style

Samson Alobalorun Bamidele; Adanze Asinobi; Ngozi Chidozie Egejuru; Peter Adebayo Idowu. Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine. Comput. Biol. Bioinform. 2020, 8(2), 52-61. doi: 10.11648/j.cbb.20200802.14

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AMA Style

Samson Alobalorun Bamidele, Adanze Asinobi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu. Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine. Comput Biol Bioinform. 2020;8(2):52-61. doi: 10.11648/j.cbb.20200802.14

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@article{10.11648/j.cbb.20200802.14,
  author = {Samson Alobalorun Bamidele and Adanze Asinobi and Ngozi Chidozie Egejuru and Peter Adebayo Idowu},
  title = {Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine},
  journal = {Computational Biology and Bioinformatics},
  volume = {8},
  number = {2},
  pages = {52-61},
  doi = {10.11648/j.cbb.20200802.14},
  url = {https://doi.org/10.11648/j.cbb.20200802.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20200802.14},
  abstract = {This study developed a model for the survival of diabetes mellitus patients in Nigeria. The study identified the variables monitored during the treatment of diabetes mellitus patients, formulated, and validated the predictive model for the survival time of diabetes mellitus patients. In order to achieve the aim of this study, structured interview with professional physicians so as to identify the variables for the survival time of diabetes mellitus with historical datasets were collected based on the variables monitored during treatment. The model was formulated using the support vector machine based on the variables identified and simulated using the WEKA Software using the historical datasets for training the model. The results showed that data collected from 29 patients at a hospital located in south-western Nigeria consisting of 32 attributes with a target class containing information about the survival time of each diabetes mellitus patient. The study concluded that the model can also be integrated into existing Health Information System (HIS) which captures and manages clinical information which can be fed to the predictive model thus improving the decisions affecting the patient’s outcome and the real-time assessment of clinical information affecting the patient’s survival of diabetes.},
 year = {2020}
}

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TY  - JOUR
T1  - Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine
AU  - Samson Alobalorun Bamidele
AU  - Adanze Asinobi
AU  - Ngozi Chidozie Egejuru
AU  - Peter Adebayo Idowu
Y1  - 2020/11/04
PY  - 2020
N1  - https://doi.org/10.11648/j.cbb.20200802.14
DO  - 10.11648/j.cbb.20200802.14
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 52
EP  - 61
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20200802.14
AB  - This study developed a model for the survival of diabetes mellitus patients in Nigeria. The study identified the variables monitored during the treatment of diabetes mellitus patients, formulated, and validated the predictive model for the survival time of diabetes mellitus patients. In order to achieve the aim of this study, structured interview with professional physicians so as to identify the variables for the survival time of diabetes mellitus with historical datasets were collected based on the variables monitored during treatment. The model was formulated using the support vector machine based on the variables identified and simulated using the WEKA Software using the historical datasets for training the model. The results showed that data collected from 29 patients at a hospital located in south-western Nigeria consisting of 32 attributes with a target class containing information about the survival time of each diabetes mellitus patient. The study concluded that the model can also be integrated into existing Health Information System (HIS) which captures and manages clinical information which can be fed to the predictive model thus improving the decisions affecting the patient’s outcome and the real-time assessment of clinical information affecting the patient’s survival of diabetes.
VL  - 8
IS  - 2
ER  -

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Author Information

Samson Alobalorun Bamidele

Department of Computer, Library and Information Science, Kwara State University, Malete, Nigeria
Adanze Asinobi

Department of Paediatrics College of Medicine, University of Ibadan, Ibadan, Nigeria
Ngozi Chidozie Egejuru

Department of Computer Science, Hallmark University, Ijebu Itele, Nigeria
Peter Adebayo Idowu

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

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Plain Text BibTeX RIS

APA Style

Samson Alobalorun Bamidele, Adanze Asinobi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu. (2020). Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine. Computational Biology and Bioinformatics, 8(2), 52-61. https://doi.org/10.11648/j.cbb.20200802.14

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ACS Style

Samson Alobalorun Bamidele; Adanze Asinobi; Ngozi Chidozie Egejuru; Peter Adebayo Idowu. Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine. Comput. Biol. Bioinform. 2020, 8(2), 52-61. doi: 10.11648/j.cbb.20200802.14

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AMA Style

Samson Alobalorun Bamidele, Adanze Asinobi, Ngozi Chidozie Egejuru, Peter Adebayo Idowu. Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine. Comput Biol Bioinform. 2020;8(2):52-61. doi: 10.11648/j.cbb.20200802.14

Copy | Download

@article{10.11648/j.cbb.20200802.14,
  author = {Samson Alobalorun Bamidele and Adanze Asinobi and Ngozi Chidozie Egejuru and Peter Adebayo Idowu},
  title = {Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine},
  journal = {Computational Biology and Bioinformatics},
  volume = {8},
  number = {2},
  pages = {52-61},
  doi = {10.11648/j.cbb.20200802.14},
  url = {https://doi.org/10.11648/j.cbb.20200802.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20200802.14},
  abstract = {This study developed a model for the survival of diabetes mellitus patients in Nigeria. The study identified the variables monitored during the treatment of diabetes mellitus patients, formulated, and validated the predictive model for the survival time of diabetes mellitus patients. In order to achieve the aim of this study, structured interview with professional physicians so as to identify the variables for the survival time of diabetes mellitus with historical datasets were collected based on the variables monitored during treatment. The model was formulated using the support vector machine based on the variables identified and simulated using the WEKA Software using the historical datasets for training the model. The results showed that data collected from 29 patients at a hospital located in south-western Nigeria consisting of 32 attributes with a target class containing information about the survival time of each diabetes mellitus patient. The study concluded that the model can also be integrated into existing Health Information System (HIS) which captures and manages clinical information which can be fed to the predictive model thus improving the decisions affecting the patient’s outcome and the real-time assessment of clinical information affecting the patient’s survival of diabetes.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine
AU  - Samson Alobalorun Bamidele
AU  - Adanze Asinobi
AU  - Ngozi Chidozie Egejuru
AU  - Peter Adebayo Idowu
Y1  - 2020/11/04
PY  - 2020
N1  - https://doi.org/10.11648/j.cbb.20200802.14
DO  - 10.11648/j.cbb.20200802.14
T2  - Computational Biology and Bioinformatics
JF  - Computational Biology and Bioinformatics
JO  - Computational Biology and Bioinformatics
SP  - 52
EP  - 61
PB  - Science Publishing Group
SN  - 2330-8281
UR  - https://doi.org/10.11648/j.cbb.20200802.14
AB  - This study developed a model for the survival of diabetes mellitus patients in Nigeria. The study identified the variables monitored during the treatment of diabetes mellitus patients, formulated, and validated the predictive model for the survival time of diabetes mellitus patients. In order to achieve the aim of this study, structured interview with professional physicians so as to identify the variables for the survival time of diabetes mellitus with historical datasets were collected based on the variables monitored during treatment. The model was formulated using the support vector machine based on the variables identified and simulated using the WEKA Software using the historical datasets for training the model. The results showed that data collected from 29 patients at a hospital located in south-western Nigeria consisting of 32 attributes with a target class containing information about the survival time of each diabetes mellitus patient. The study concluded that the model can also be integrated into existing Health Information System (HIS) which captures and manages clinical information which can be fed to the predictive model thus improving the decisions affecting the patient’s outcome and the real-time assessment of clinical information affecting the patient’s survival of diabetes.
VL  - 8
IS  - 2
ER  -

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