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2026 Vol.17, Issue 1 Preview Page

General Article

Blockchain-enabled IoT and machine learning framework for health prediction in smart and healthy urban environments
Jainendra Singh1
,
Nitin Goyal2
,
Atamkur Anitha3
,
Shikha Rani4
,
Gaurav Sharma5*
,
Bharti Sharma67
,
Ravneet Kaur8
,
Praveen Yadav9
,
Mukta Garg10
1Research Scholar, Engineer, Centre Development for Intelligence System (CDIS), Indian Institute of Technology, Kanpur, India
2Assistant Professor, Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, 123031, Haryana, India
3Sr. Assistant Professor, ECE Department, CVR College of Engineering, Hyderabad, Telangana, India
4Assistant Professor, Department of Electronics & Communication Engineering, G.L. Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh 201306, India
5Associate Professor, School of Artificial Intelligence, Bennett University, Greater Noida, Uttar Pradesh, India
6Adjunct Faculty, Department of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam 602105, Chennai, Tamilnadu, India
7Assistant Professor, Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India
8Associate Professor, School of Engineering and Technology, CGC University Mohali-140307, Punjab, India
9Program Manager, HCL Technologies, Canada
10Senior Delivery Manager, The Guardian Life Insurance Company, Canada
*Corresponding Author
31 March 2026. pp. 118-137
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Abstract

As part of the movement toward healthier and more sustainable built environments, emerging digital technologies offer powerful solutions for proactive health monitoring and disease prevention. This study presents an integrated framework that leverages Internet of Things (IoT) sensor systems, machine learning (ML) models, and blockchain-enabled data management to support real-time cardiovascular risk prediction and secure health information handling within smart and health-oriented urban settings. In the proposed system, IoT-based wearable sensors such as smartwatches continuously capture physiological signals relevant to cardiovascular health, enabling seamless monitoring within daily living environments. The collected data are processed using multiple ML algorithms, including Random Forest, Decision Tree, Logistic Regression, Multi-layer Perceptron, K-Nearest Neighbour, and Gradient Boosting, trained on a Kaggle-sourced cardiovascular disease (CVD) dataset securely stored in a cloud repository. Among these, the Gradient Boosting classifier achieved the highest prediction accuracy of 72%, supporting timely identification of at-risk individuals and enabling early preventive interventions. To ensure data integrity, privacy, and traceability across healthcare ecosystems, blockchain technology is incorporated as a secure layer for storing and managing sensitive patient information. This enhances trust, transparency, and compliance in digital health services integrated within sustainable smart buildings and urban infrastructures. Overall, the proposed approach strengthens the role of IoT-enabled health monitoring as an integral component of sustainable living environments, contributing to improved healthcare accessibility, reduced clinical burden, and better long-term management of cardiovascular conditions. The integration of sensor technologies, ML-based analytics, and secure data architectures supports the development of future-ready healthy buildings and smart urban systems designed to enhance occupant well-being.

Keywords
IoT sensors
machine learning
smart and healthy buildings
sustainable urban development
health monitoring systems
wearable devices
predictive analytics
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Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 17
  • No :1
  • Pages :118-137
  • Received Date : 2025-12-25
  • Accepted Date : 2026-01-30
  • DOI :https://doi.org/10.22712/susb.20260008
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