Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar Wiki for Call for Papers WorldCat Zenodo

IoT-based Remote Patient Monitoring System for Continuous Chronic Disease Management: A Machine Learning Approach

Author(s) Mr MAHESH KRISHNA, Mr IRIMPAN ARNOLD, Dr KANNAGI ANBAZHAGAN
Country India
Abstract Customer churn prediction represents a critical challenge in telecommunications,
where acquisition costs significantly exceed retention expenses. Diabetes
management represents a critical challenge in modern healthcare, where continuous monitoring costs significantly exceed episodic treatment expenses. This research presents a systematic analysis of an IoT-based Remote Patient Monitoring (RPM) system for diabetic patients, integrating wearable sensors, cloud computing, and machine learning for real-time health tracking. Using ESP32 microcontroller architecture with MAX30102 heart rate sensors, glucose monitors, and temperature sensors, we implement comprehensive data pipeline including wireless transmission via MQTT protocol, cloud-based storage, and anomaly detection algorithms. The system addresses the 26.5% gap in continuous care delivery for middle-class diabetic populations, where traditional hospital-based monitoring proves economically prohibitive. Results demonstrate that real-time monitoring enables proactive intervention, reducing emergency hospitalizations by 40% while lowering long-term treatment costs by 30% compared to conventional periodic check-ups. Feature analysis reveals
glucose level fluctuations, heart rate variability, and body temperature as
primary health indicators requiring immediate intervention. We develop an
integrated alert framework connecting predictive outputs to mobile applications
and healthcare provider dashboards for immediate response. This research
advances both theoretical understanding of remote healthcare dynamics and practical implementation strategies for medical institutions seeking cost-effective
continuous monitoring capabilities.
Field Computer > Electronics
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-29
View / Download PDF File

Share this



E-ISSN 3048-7641
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJFR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-79-39-59 editor@aijfr.com