Capital University of Science and Technology, Islamabad 44000, Pakistan.
National University of Computer and Emerging Sciences (NUCES), Islamabad 44000, Pakistan.
J Healthc Eng. 2021 Nov 1;2021:2621655. doi: 10.1155/2021/2621655. eCollection 2021.
Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science that can facilitate continuous monitoring of physiological parameters-blood pressure, cholesterol levels, blood glucose, etc. The futuristic values of these critical physiological or vital sign parameters not only enable in-time assistance from medical experts and caregivers but also help patients manage their health status by receiving relevant regular alerts/advice from healthcare practitioners. In this study, we propose a machine-learning-based prediction and classification system to determine futuristic values of related vital signs for both cardiovascular and chronic respiratory diseases. Based on the prediction of futuristic values, the proposed system can classify patients' health status to alarm the caregivers and medical experts. In this machine-learning-based prediction and classification model, we have used a real vital sign dataset. To predict the next 1-3 minutes of vital sign values, several regression techniques (i.e., linear regression and polynomial regression of degrees 2, 3, and 4) have been tested. For caregivers, a 60-second prediction and to facilitate emergency medical assistance, a 3-minute prediction of vital signs is used. Based on the predicted vital signs values, the patient's overall health is assessed using three machine learning classifiers, i.e., Support Vector Machine (SVM), Naive Bayes, and Decision Tree. Our results show that the Decision Tree can correctly classify a patient's health status based on abnormal vital sign values and is helpful in timely medical care to the patients.
心血管疾病和慢性呼吸道疾病是全球公共卫生的重大威胁，每年在全球造成约 1900 万人死亡。通过利用医学科学的技术进步，可以持续监测生理参数（如血压、胆固醇水平、血糖等），从而降低这种高死亡率。这些关键生理或生命体征参数的未来值不仅能使医疗专家和护理人员及时提供帮助，还能帮助患者通过接收医疗从业者的相关定期提醒/建议来管理自己的健康状况。在本研究中，我们提出了一个基于机器学习的预测和分类系统，用于确定心血管疾病和慢性呼吸道疾病相关生命体征的未来值。基于未来值的预测，所提出的系统可以对患者的健康状况进行分类，向护理人员和医疗专家发出警报。在这个基于机器学习的预测和分类模型中，我们使用了真实的生命体征数据集。为了预测下一个 1-3 分钟的生命体征值，我们测试了几种回归技术（即线性回归和 2、3、4 阶的多项式回归）。对于护理人员，我们使用 60 秒的预测，为了方便紧急医疗援助，我们使用 3 分钟的生命体征预测。根据预测的生命体征值，使用三种机器学习分类器（即支持向量机 (SVM)、朴素贝叶斯和决策树）评估患者的整体健康状况。我们的结果表明，决策树可以根据异常生命体征值正确地对患者的健康状况进行分类，有助于及时对患者进行医疗护理。
