Artificial Intelligence of Things - based Predictive Digital Health System
Year: 2025
Type: article
Abstract: This study presents an Artificial Intelligence of Things (AIoT)-based system designed to address a key challenge in digital health by integrating sensor data, IoT platforms, and machine learning algorithms. The project’s main objective is to provide a predictive and real-time monitoring solution capable of identifying critical health conditions at an early stage. The methodology involves sensor data acquisition, preprocessing, and cloud integration combined with predictive models such as decision trees, ensemble methods, and deep learning. Experimental results demonstrate the feasibility and efficiency of the proposed architecture. The system achieved robust accuracy levels across multiple models, highlighting the superiority of ensemble approaches in predictive performance. The discussion emphasizes both the scientific implications of the methodology and its practical impact for medical monitoring and intervention. The proposed solution outlines future perspectives for integrating multimodal data, enhancing cybersecurity, and extending applicability to resource-limited healthcare contexts.
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7220105536