Key result
Random Forest model outperforms SVM and KNN for analyzing remote vital signs in AI monitoring.
Why the study?
Persistent challenges in data reliability, privacy protection, and computational limitations hinder effective remote health monitoring using AI and IoT technologies.
An AI-powered IoT remote health monitoring framework using a Random Forest model demonstrates superior accuracy in analyzing vital signs, supporting real-time decision-making.
Answers are grounded in this paper and cite their sources.
May support AI model development for remote monitoring; leaves open prospective clinical validation before practice adoption.
The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed modern healthcare, enabling smarter and more efficient methods for patient monitoring and disease management. This study presents the design and optimization of an AI-powered remote health monitoring system that utilizes IoT-enabled sensors and machine learning algorithms to enhance healthcare delivery. The research emphasizes key objectives such as improving diagnostic accuracy, ensuring real-time data analysis, maintaining data security, and achieving seamless system integration to support proactive and personalized patient care. To address persistent challenges related to data reliability, privacy protection, and computational limitations, the study proposes an innovative framework for continuous health monitoring. Several machine learning techniques—including Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—are applied to analyze vital health indicators such as heart rate, blood pressure, and electrocardiogram (ECG) signals. Experimental results reveal that the Random Forest model delivers superior performance compared to other algorithms, achieving higher accuracy, precision, and recall. The findings highlight the strong potential of AI-driven health monitoring systems in supporting real-time decision-making and personalized healthcare. Moreover, this research lays a solid foundation for future advancements in cost-effective, secure, and intelligent healthcare solutions, paving the way for more adaptive and patient-centered medical technologies.
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Arif et al. (2025) studied this question. Random Forest (RF) model vs. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) was evaluated on Accuracy, precision, and recall for analyzing vital health indicators. An AI-powered remote health monitoring system using the Random Forest model delivered superior accuracy, precision, and recall for analyzing vital signs compared to SVM and KNN algorithms.
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