An Ensemble Model Health Care Monitoring System
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a blockchain-based Internet of Things (IoT) system for secure patient monitoring. The ensemble classification model achieved 95.56% accuracy in identifying diseases like COPD and heart disease.
Area Of Science
- Health Informatics
- Cybersecurity
- Machine Learning
Background
- Internet of Things (IoT) enhances healthcare with patient monitoring.
- Data security and privacy are critical concerns in IoT healthcare.
- Blockchain technology offers robust data integrity and security solutions.
Purpose Of The Study
- To develop a secure and privacy-preserving IoT healthcare monitoring system.
- To integrate blockchain technology with IoT for enhanced data protection.
- To implement an ensemble classification model for accurate disease detection.
Main Methods
- Data generation for chronic obstructive pulmonary disease (COPD), lung cancer, and heart disease.
- Preprocessing IoT healthcare data using enhanced scalar normalization.
- Feature extraction including mutual information, statistical features, and entropy.
- Ensemble classification using deep maxout, improved deep convolutional network (IDCNN), and deep belief network (DBN).
Main Results
- The ensemble model achieved a highest accuracy of 95.56% in disease classification.
- The system demonstrated superior performance compared to traditional classifiers like neural network (89.08%) and LSTM (80.63%).
- Accurate classification was achieved at a 60% learning percentage.
Conclusions
- The proposed blockchain-based IoT system effectively addresses security and privacy challenges in patient monitoring.
- Ensemble classification significantly improves the accuracy of disease detection in healthcare.
- This integrated approach offers a promising advancement for secure and reliable IoT healthcare solutions.
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