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INTEGRATION OF THE INTERNET OF THINGS AND MACHINE LEARNING FOR THE DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR MONITORING PATIENT HEALTH AND ENVIRONMENTAL FACTORS

https://doi.org/10.53360/2788-7995-2025-3(19)-2

Abstract

The article examines prospects for integrating Internet of Things (IoT) technologies and machine learning (ML) algorithms to create an intelligent air-quality monitoring system. It additionally describes a patient physiological-monitoring module – covering heart rate (HR), HRV metrics (SDNN, RMSSD, LF/HF), respiratory rate, SpO₂, and blood pressure (BP) – integrated into a unified IoT architecture and data-analysis pipeline. A prototype is presented with streaming aggregation of medical and environmental signals, early-warning rules, and a scenario for jointly interpreting air parameters and patient status. The focus is on IoT sensors, real-time data processing methods, and air-pollution forecasting using hybrid ML models, including Random Forest, Gradient Boosting Machines, and Long Short-Term Memory (LSTM) networks. The importance of improving sensor sensitivity and reliability through nanomaterials such as polyaniline, graphene, and carbon nanotubes is emphasized. The article highlights data protection, energy efficiency, and the resilience of scalable IoT systems, as well as their role in reducing environmental risks and supporting the «smart city» concept. It considers pathways for integrating such systems into urban infrastructure, including solutions for automated data analysis and visualization. The article also discusses prospects for deploying intelligent monitoring systems in industrial and residential infrastructure to enhance environmental oversight. Particular attention is paid to developing forecasting models that account for seasonal and climatic variations affecting pollution levels. An interdisciplinary approach that combines advances in IoT, nanotechnology, and ML is underscored as essential for addressing sustainable urban development challenges. The presented results demonstrate high effectiveness and practical applicability for controlling air pollution, improving public health, protecting the environment, and promoting sustainable urban development.

About the Authors

Zh. E. Baigarayeva
Al-Farabi Kazakh National University
Kazakhstan

Zhanel Yermashkyzy Baigarayeva – master of technical sciences, 3rd year PhD student

050040, Kazakhstan, Almaty, Al Farabi av.71 



B. T. Imanbek
Al-Farabi Kazakh National University
Kazakhstan

Baglan Talgatkyzy Imanbek – PhD, Acting Associate Professor, Senior Lecturer

050040, Kazakhstan, Almaty, Al Farabi av.71 



A. K. Boltaboyeva
Al-Farabi Kazakh National University
Kazakhstan

Assiya Kublandikyzi Boltaboyeva – master of technical sciences, 1st year PhD student

050040, Kazakhstan, Almaty, Al Farabi av.71 



D. Turmakhanbet
Al-Farabi Kazakh National University
Kazakhstan

050040, Kazakhstan, Almaty, Al Farabi av.71 



G. A. Amirkhanova
Al-Farabi Kazakh National University
Kazakhstan

 Gulshat Amanzholovna Amirkhanova – PhD, Senior Lecturer 

050040, Kazakhstan, Almaty, Al Farabi av.71 



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Review

For citations:


Baigarayeva Zh.E., Imanbek B.T., Boltaboyeva A.K., Turmakhanbet D., Amirkhanova G.A. INTEGRATION OF THE INTERNET OF THINGS AND MACHINE LEARNING FOR THE DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR MONITORING PATIENT HEALTH AND ENVIRONMENTAL FACTORS. Bulletin of Shakarim University. Technical Sciences. 2025;(3(19)):11-21. (In Russ.) https://doi.org/10.53360/2788-7995-2025-3(19)-2

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ISSN 2788-7995 (Print)
ISSN 3006-0524 (Online)
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