ArticleSensors (Basel, Switzerland)2023
An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications.
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 3 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- 5G Bedside Interactive Terminal Health Education to Enhance Treatment Adherence in Elderly Patients with Chronic Diseases: The Mediating Role of Self-Efficacy.Patient preference and adherence · 2026Article
- Enhancing healthcare IoT systems for diabetic patient monitoring: Integration of Harris Hawks and grasshopper optimization algorithms.PloS one · 2024Article
- Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis.Indian journal of endocrinology and metabolismReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundContinuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. MAIN PROBLEM: Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. METHODOLOGY: This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO).
resultsCompared to other techniques, the simulation's outcomes demonstrate that the suggested approach offers greater accuracy.
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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.