What question did this study set out to answer?

This research aims to develop a secure framework for the early diagnosis of heart diseases using IoT technology and machine learning in low-resource settings.

June 21, 2026

IoT-enabled hybrid machine learning framework for early heart disease diagnosis using lightweight cryptography and optimized classifiers for resource-constrained healthcare environments

Key Points

This research aims to develop a secure framework for the early diagnosis of heart diseases using IoT technology and machine learning in low-resource settings.
Integrated a hybrid machine learning framework with IoT using lightweight encryption for data security.
Utilized nature-inspired optimization for feature selection and classifier tuning.
Evaluated solution using various cardiac datasets under inconsistent IoT conditions.
Achieved high accuracy and interpretability in heart disease diagnosis.
Demonstrated robust performance in resource-constrained environments with a focus on security and efficiency.
Verified practical deployment suitability for large-scale IoT healthcare systems.

Abstract

The large-scale application of IoT technology in the medical industry also brings the advantage of continuous monitoring of patients and timely detection of heart diseases; however, it also results in challenges like ensuring data privacy, resources, and management of medical records. The paper presents a secure hybrid machine learning framework incorporated with IoT for diagnosing heart diseases at their infant stage, mostly meant for healthcare settings with low resources. The proposed protocol integrates lightweight encryption to secure medical data during early-stage diagnosis and data transmission in IoT-based healthcare systems combined with machine learning models, while nature-inspired optimization improves feature selection, classifier tuning, accuracy, and convergence. A variety of cardiac datasets are extensively used for the system’s proposal, and it is evaluated under inconsistent and noisy IoT conditions to determine its generalization capacity, robustness, and interpretability, which are assessed through the deployment of explainable artificial intelligence techniques. The framework is also analyzed regarding scalability, computational efficiency, and security – performance trade-offs to verify its suitability for real-time deployment in large-scale IoT healthcare systems. The findings of the study indicate that the suggested method has a well-balanced integration of security, accuracy, interpretability, and efficiency, thereby giving a practical and reliable solution for intelligent IoT-based healthcare diagnostics.

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