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Explainable context-aware IoT framework using human digital twin for healthcare
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AI
DOI：10.1007/s11042-023-16922-5.png)
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Abstract
En 中文
Health, being a vital aspect of a fulfilling life, has experienced advancements through improved medical services in alignment with Sustainable Development Goal 3 (SDG 3). Embracing technologies like IoT and Human Digital Twin (HDT), this research introduces an innovative and explainable context-aware IoT framework. A HDT is a virtual replica of an individual that combines data from various sources to provide insights into their behavior, health, and preferences. By harnessing wearable sensors, machine learning, and HDT technology, the framework aims to predict cardiovascular disease (CVD) at an early stage, thereby extending life expectancy. Notably, CVD affects approximately 18.56 million individuals worldwide. By leveraging data from wearable sensors, such as blood pressure, glucose levels, and activity monitoring, advanced machine learning algorithms are employed to achieve highly accurate early predictions in this study. The research further involves a thorough examination of patient health records, identifying significant CVD features. This research presents a comprehensive analysis of a CVC dataset comprising 70,000 patient records and 11 features, using machine learning models to predict and diagnose cardiovascular disease. Among the eight models implemented, XG Boost achieved an impressive accuracy of 88.91%, surpassing existing accuracies for cardiac arrest (85%) and general disease prediction (84.5%). The proposed model not only facilitates the integration of sufferers and doctors in a scalable and efficient environment but also showcases how the incorporation of Human Digital Twin (HDT) technology enhances healthcare system operations and services. The results highlight the significant contribution in improving predictive accuracy and enabling a more effective and informed approach to patient care.
Keywords:
Explainable healthcare system
Internet of things
Digital twin
Cardiovascular disease
Machine learning
Wearable sensors
Journal
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3
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1.9W
Citations:
3.2W
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