At present days, Internet of Things (IoT) and cloud platforms become widely used in various healthcare applications. The enormous quantity of data produced by the IoT devices in the healthcare sector can be examined on the cloud platform instead of dependent on restricted storage and computation resources exist in the mobile gadgets. For offering effective medicinal services, in this article, an online medical decision support system (OMDSS) is introduced for chronic kidney disease (CKD) prediction. The presented model involves a set of stages namely data gathering, preprocessing, and classification of medical data for the prediction of CKD. For classification, logistic regression (LR) model is applied for classifying the data instances into CKD and non‐CKD. In addition, for tuning the parameters of LR, Adaptive Moment Estimation (Adam), and adaptive learning rate optimization algorithm is applied. The performance of the introduced model is examined using a benchmark CKD dataset. The experimental outcome observed the superior characteristics of the presented model on the applied dataset.

中文翻译：

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具有云中心医疗决策支持系统的智能物联网，用于慢性肾脏疾病预测

如今，物联网（IoT）和云平台已广泛用于各种医疗保健应用程序中。可以在云平台上检查医疗保健部门中IoT设备产生的大量数据，而不必依赖移动设备中存在的受限存储和计算资源。为了提供有效的医疗服务，本文引入了在线医疗决策支持系统（OMDSS），用于预测慢性肾脏病（CKD）。提出的模型涉及一组阶段，即数据收集，预处理和医学数据分类，以预测CKD。对于分类，应用逻辑回归（LR）模型将数据实例分类为CKD和非CKD。另外，为了调整LR的参数，自适应矩估计（Adam），并采用自适应学习率优化算法。使用基准CKD数据集检查引入模型的性能。实验结果在所应用的数据集上观察到了所提出模型的优越特性。