Menon SP, Shukla PK, Sethi P, Alasiry A, Marzougui M, Alouane MTH, Khan AA. An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications.
SENSORS (BASEL, SWITZERLAND) 2023;
23:3004. [PMID:
36991714 PMCID:
PMC10052330 DOI:
10.3390/s23063004]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/19/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND
Continuous 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).
RESULTS
Compared to other techniques, the simulation's outcomes demonstrate that the suggested approach offers greater accuracy.
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