Abstract

The proliferation of wireless sensor networks (WSNs) in the health care sector has enabled continuous, real-time monitoring of patients’ vital signs, enabling early diagnosis and prompt care. Still, the healthcare data classification techniques currently in use face critical challenges, including high computational cost and slow convergence, as well as limited scalability for large, real-time data streams. This research proposes the Optimized Healthcare Data Classification Model (OHD-Classifier) to streamline real-time monitoring systems by integrating Support Vector Machines (SVMs) with Stochastic Gradient Descent (SGD) (OHD-SVMSGD). As described, the OHD-Classifier advances SGD-trained SVMs, thereby accelerating convergence and improving efficiency. With this advancement, the OHD-Classifier improves classification accuracy for vital healthcare conditions, including irregular heart rates, critical- and low-variance hypertensive episodes, and other essential sign anomalies. The primary function of the model remains real-time data processing from WSNs, with provisions for instantaneous feedback to enable effective healthcare staff and decision-making. The solutions available today are hampered by slow model training due to the high dimensionality of the feature space, overfitting to WSNs, and unstable input data streams. The OHD-Classifier addresses the issues described through effective feature selection and the optimization of SVM parameters by SGD. This not only streamlines model training but also progresses the model’s generalization to novel data. Outcomes indicate the OHD-Classifier outperforms all competing models on accuracy, training speed, and adaptability. The proposed model has the accuracy as 98%, Precision as 97%, Recall as 97% and F1 Score as 99%. This research lays the groundwork for developing more efficient, scalable monitoring systems in the healthcare sector and for improving patient care and outcomes in fast-changing, low-resource environments.