LAPSE:2019.0862
Published Article
LAPSE:2019.0862
An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria
July 31, 2019
The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes.
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Keywords
clinical implications, cluster, data mining, Decision table, diabetes, epidemiology, forecast, Machine Learning, PART, real-life patients, regression, Weka
Subject
Suggested Citation
Sohail MN, Jiadong R, Muhammad MU, Chauhdary ST, Arshad J, Verghese AJ. An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria. (2019). LAPSE:2019.0862
Author Affiliations
Sohail MN: Department of Information Sciences and Technology, Yanshan University, Qinhuangdao 066000, China [ORCID]
Jiadong R: Department of Information Sciences and Technology, Yanshan University, Qinhuangdao 066000, China
Muhammad MU: Department of Information Sciences and Technology, Yanshan University, Qinhuangdao 066000, China
Chauhdary ST: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 43600, Pakistan
Arshad J: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 43600, Pakistan [ORCID]
Verghese AJ: Department of Management, American Hotel and Lodging Association, New York, NY 10006, USA
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Jiadong R: Department of Information Sciences and Technology, Yanshan University, Qinhuangdao 066000, China
Muhammad MU: Department of Information Sciences and Technology, Yanshan University, Qinhuangdao 066000, China
Chauhdary ST: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 43600, Pakistan
Arshad J: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 43600, Pakistan [ORCID]
Verghese AJ: Department of Management, American Hotel and Lodging Association, New York, NY 10006, USA
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Journal Name
Processes
Volume
7
Issue
5
Article Number
E289
Year
2019
Publication Date
2019-05-15
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr7050289, Publication Type: Journal Article
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Published Article
LAPSE:2019.0862
This Record
External Link
doi:10.3390/pr7050289
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Version History
[v1] (Original Submission)
Jul 31, 2019
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Jul 31, 2019
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https://psecommunity.org/LAPSE:2019.0862
Original Submitter
Calvin Tsay
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