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International Journal of Science, Strategic Management and Technology

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AI-DRIVEN SMART HEALTHCARE MONITORING SYSTEM FOR REAL-TIME PATIENT HEALTH ASSESSMENT AND PREDICTIVE DISEASE RISK ANALYSIS

AUTHORS:
Akula Sathvika
Puttapaka Lokesh
Mentor
P Kavitha
Affiliation
Department Of ECE, SVS Group of Institutions, Hanumakonda, Telangana
DOI: 10.55041/ijsmt.v2i7.011
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
The rapid growth of healthcare technologies and the increasing demand for continuous patient care have led to the development of AI-driven smart healthcare monitoring systems. These systems integrate artificial intelligence (AI), Internet of Things (IoT) devices, wearable sensors, and cloud computing to monitor patients' health conditions in real time. By collecting physiological data such as heart rate, blood pressure, body temperature, oxygen saturation, and activity levels, the system can analyze health patterns and detect abnormalities at an early stage. Machine learning algorithms enable accurate prediction of potential health risks, allowing healthcare providers to take preventive measures and improve patient outcomes. Furthermore, remote monitoring capabilities reduce hospital visits, lower healthcare costs, and enhance access to medical services, especially for elderly and chronically ill patients. The proposed AI-driven smart healthcare monitoring system offers a reliable, efficient, and scalable solution for personalized healthcare management by providing real-time alerts, data analytics, and decision support for both patients and medical professionals.
Keywords
Artificial Intelligence (AI) Smart Healthcare Healthcare Monitoring System Internet of Things (IoT) Wearable Sensors Machine Learning Remote Patient Monitoring Predictive Analytics Real-Time Health Monitoring Cloud Computing Personalized Healthcare.
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 7
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Jul 06 2026
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Sathvika, A. & Lokesh, P. (2026). AI-Driven Smart Healthcare Monitoring System for Real-Time Patient Health Assessment and Predictive Disease Risk Analysis. International Journal of Science, Strategic Management and Technology, 02(7). https://doi.org/10.55041/ijsmt.v2i7.011

Sathvika, Akula, and Puttapaka Lokesh. "AI-Driven Smart Healthcare Monitoring System for Real-Time Patient Health Assessment and Predictive Disease Risk Analysis." International Journal of Science, Strategic Management and Technology, vol. 02, no. 7, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i7.011.

Sathvika, Akula, and Puttapaka Lokesh. "AI-Driven Smart Healthcare Monitoring System for Real-Time Patient Health Assessment and Predictive Disease Risk Analysis." International Journal of Science, Strategic Management and Technology 02, no. 7 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i7.011.

References

  1. Kumar, S., & Singh, R. (2022). IoT-based smart healthcare monitoring systems: A survey. IEEE Access.

  2. Patel, V., & Shah, D. (2021). Machine learning approaches for healthcare data analysis and disease prediction. International Journal of Medical Informatics.

  3. World Health Organization (WHO). (2023). Digital health and AI in healthcare. https://www.who.int

  4. Zhang, Y., & Yang, L. (2020). Wearable sensors for remote patient monitoring systems. Sensors (MDPI).

  5. IEEE Standards Association. (2022). IoT and healthcare communication standards. https://www.ieee.org

  6. Gupta, A., & Mehta, P. (2021). Cloud computing in healthcare applications. Journal of Cloud Computing.

  7. Rashid, H., et al. (2023). Deep learning for medical diagnosis systems. Elsevier Biomedical Engineering Reviews.

  8. Li, X., & Wang, J. (2020). AI-based health monitoring using wearable devices. IEEE Sensors Journal.

  9. Brown, T. (2021). Remote patient monitoring systems and applications. Springer Healthcare Technology.

  10. Chen, M., et al. (2022). Edge computing in smart healthcare systems. Future Generation Computer Systems.

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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