ArticleSensors (Basel, Switzerland)2023
A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the application of IoT mobile app and healthcare services for diabetic elderly: A systematic review.PloS one · 2025Pooled it
- IoT-enabled solutions for paediatric diabetes: examining consumer readiness and adoption factors.Frontiers in digital health · 2026Article
- Subtypes of digital literacy and their association with innovative behavior among Chinese undergraduate nursing students: a latent profile analysis.Frontiers in public health · 2026Article
- AI-optimized GRU-based self-attention model for predictive diabetes staging in IoT healthcare 5.0.Scientific reports · 2025Article
- A Deep Learning Framework for Early Parkinson's Disease Detection: Leveraging Spiral and Wave Handwriting Tasks with EfficientNetV2-S.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device.Sensors (Basel, Switzerland) · 2024Article
- Cross-sectional associations of self-perceived stress and hair cortisol with metabolic outcomes and microvascular complications in type 2 diabetes.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient's health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient's condition accurately and sends it to the doctor's portal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.