Version 1
: Received: 26 May 2024 / Approved: 28 May 2024 / Online: 28 May 2024 (14:33:33 CEST)
How to cite:
Adeleke, O.; Aroba, O. J.; Adebayo, S.; Aworinde, H.; Adeleke, O.; Adeniyi, A. E. Impact of a Workplace Screening Programme for Hypertension: A 5-Year Machine Learning-Based Analysis of a University Workforce Medical Records. Preprints2024, 2024051861. https://doi.org/10.20944/preprints202405.1861.v1
Adeleke, O.; Aroba, O. J.; Adebayo, S.; Aworinde, H.; Adeleke, O.; Adeniyi, A. E. Impact of a Workplace Screening Programme for Hypertension: A 5-Year Machine Learning-Based Analysis of a University Workforce Medical Records. Preprints 2024, 2024051861. https://doi.org/10.20944/preprints202405.1861.v1
Adeleke, O.; Aroba, O. J.; Adebayo, S.; Aworinde, H.; Adeleke, O.; Adeniyi, A. E. Impact of a Workplace Screening Programme for Hypertension: A 5-Year Machine Learning-Based Analysis of a University Workforce Medical Records. Preprints2024, 2024051861. https://doi.org/10.20944/preprints202405.1861.v1
APA Style
Adeleke, O., Aroba, O. J., Adebayo, S., Aworinde, H., Adeleke, O., & Adeniyi, A. E. (2024). Impact of a Workplace Screening Programme for Hypertension: A 5-Year Machine Learning-Based Analysis of a University Workforce Medical Records. Preprints. https://doi.org/10.20944/preprints202405.1861.v1
Chicago/Turabian Style
Adeleke, O., Oludamola Adeleke and Abidemi Emmanuel Adeniyi. 2024 "Impact of a Workplace Screening Programme for Hypertension: A 5-Year Machine Learning-Based Analysis of a University Workforce Medical Records" Preprints. https://doi.org/10.20944/preprints202405.1861.v1
Abstract
The burden of hypertension remains unacceptably high globally, particularly in low- and middle-income countries (LMICs). Workplace offer enormous potential as an idea setting for early detection and treatment of hypertension among the working class. Analysis of such a Workplace Screening Programme can reveal information about its potential impact. Machine learning techniques such as k-Means Clustering are untapped tools for such analyses. We set out to deploy this tool for the analysis of our university annual medical screening of workforce for hypertension. An anonymized dataset containing the demography and blood pressure measurements values obtained from staffs of different departments/units in an academic institution was obtained. The total number of samples or data points are 1, 723 in which the input dataset contains six features, including year category (2018, 2019, 2021,2022), Department/Unit (academic and non-academic), gender (male and female), while the target output is the blood pressure status (low, normal and high) respectively. Analysis of the dataset was carried out using machine learning techniques. In this retrospective analysis, it was observed that the mean age for this working class is 42 years old. Similarly, it was discovered that hypertension was prevalent among members of staff above the age of 40 irrespective of their gender or professional category (academic or non-academic). The analysis also revealed that there was a steady decline in the prevalence of hypertension from 2018 to 2022. From the research, it is evident that the adoption of machine learning techniques for periodic analyses of workplace health status screening initiative (especially for hypertension) is effective, feasible, and sustainable to diagnose and control hypertension among the working class.
Keywords
Machine learning, Hypertension, Analysis, Prediction, Medical record.
Subject
Medicine and Pharmacology, Clinical Medicine
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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