Computational Biology and Bioinformatics
Volume 8, Issue 2, December 2020, Pages: 52-61
Received: May 30, 2020;
Accepted: Oct. 23, 2020;
Published: Nov. 4, 2020
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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
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.
Samson Alobalorun Bamidele,
Ngozi Chidozie Egejuru,
Peter Adebayo Idowu,
Survival Model for Diabetes Mellitus Patients’ Using Support Vector Machine, Computational Biology and Bioinformatics.
Vol. 8, No. 2,
2020, pp. 52-61.
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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