Adeleke Olumide, Adebayo Segun, Aworinde Halleluyah, Adeleke Oludamola, Adeniyi Abidemi Emmanuel, Aroba Oluwasegun Julius
Directorate of Health Services, Bowen University, Iwo, Nigeria.
College of Health Sciences, Bowen University, Iwo, Nigeria.
Sci Rep. 2024 Dec 4;14(1):30255. doi: 10.1038/s41598-024-74360-1.
The global prevalence of hypertension continues excessively elevated, especially among low- and middle-income nations. Workplaces provide tremendous opportunities as a unique, easily accessible and practical avenue for early diagnosis and treatment of hypertension among the workforce class. The evaluation of such a Workplace Screening Strategy can give insight into its possible effects. Innovative machine learning approaches like k-means clustering are underutilized for such assessments. We set out to use this technology to analyze the results of our university's yearly health checkup of the employees for hypertension. An anonymized dataset including the demographics and blood pressure monitoring information gathered from workers in various departments/units of a learning organization. The overall amount of samples or data values is 1, 723, and the supplied dataset includes six attributes, such as year group (2018, 2019, 2021, 2022), Department/Unit (academic and non-academic), and gender (male and female), with the intended output being the blood pressure status (low, normal, and high). The dataset was analyzed using machine learning approaches. In this longitudinal study, it was discovered that the average age for the workforce is 42. Similarly, it was revealed that hypertension was common among employees over the age of 40, regardless of gender or occupational type (academic or nonacademic). The data also found that there was a consistent drop in the prevalence of hypertension from 2018 to 2022. According to the study findings, the use of machine learning algorithms for periodic evaluations of workplace health status monitoring initiatives (particularly for hypertension) is feasible, realistic, and sustainable in diagnosing and controlling hypertension among those in the workforce.
全球高血压患病率持续居高不下，尤其是在低收入和中等收入国家。工作场所作为一个独特、易于进入且实用的途径，为在职人员高血压的早期诊断和治疗提供了巨大机会。对这种工作场所筛查策略进行评估可以深入了解其可能产生的影响。像k均值聚类这样的创新机器学习方法在这类评估中未得到充分利用。我们着手使用这项技术来分析我校员工年度健康检查中高血压的检查结果。一个匿名数据集，包括从一个学习机构各部门/单位的工作人员收集的人口统计学和血压监测信息。样本或数据值的总量为1723，提供的数据集包括六个属性，如年份组（2018年、2019年、2021年、2022年）、部门/单位（学术和非学术）以及性别（男和女），预期输出为血压状况（低、正常和高）。使用机器学习方法对该数据集进行了分析。在这项纵向研究中，发现在职人员的平均年龄为42岁。同样，研究表明，40岁以上的员工中高血压很常见，无论性别或职业类型（学术或非学术）。数据还发现，从2018年到2022年，高血压患病率持续下降。根据研究结果，使用机器学习算法对工作场所健康状况监测举措（特别是针对高血压）进行定期评估，在诊断和控制在职人员的高血压方面是可行、现实且可持续的。
