⚡ Quick Summary
This study introduces a novel approach called Modified Coati Optimization Driven Blockchain for Healthcare Disease Detection and Classification (MCODBC-HDDC), which leverages deep learning and blockchain technology to enhance disease diagnosis accuracy. The method achieved an impressive accuracy of 97.36% in disease detection, showcasing its potential for sustainable healthcare solutions.
🔍 Key Details
- 📊 Dataset: HD dataset
- 🧩 Features used: Medical sensor data
- ⚙️ Technology: Deep Learning, Blockchain, Modified Coati Optimization
- 🏆 Performance: Accuracy of 97.36%
🔑 Key Takeaways
- 🔍 Innovative Approach: The MCODBC-HDDC method integrates blockchain for secure data management.
- 🤖 Deep Learning: Utilizes advanced deep learning techniques for accurate disease classification.
- 📈 High Accuracy: Achieved a remarkable accuracy of 97.36% in disease detection.
- 🔒 Data Privacy: Ensures patient data privacy through a decentralized and tamper-proof environment.
- 🌱 Sustainable Healthcare: Aims to improve healthcare sustainability through efficient disease diagnosis.
- 🧠 Feature Optimization: Employs the Spotted Hyena Optimization Algorithm for optimal feature selection.
- 📊 Preprocessing: Implements Z-score normalization to enhance model performance.
- 📅 Future Research: Opens avenues for further exploration in AI-driven healthcare solutions.
📚 Background
The healthcare sector faces increasing challenges due to the growing number of patients and the emergence of new diseases. Traditional methods of health monitoring often struggle with the complexity of big data generated by medical sensors. The need for accurate disease identification and patient classification has never been more critical. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning (DL), have shown promise in addressing these challenges, making early disease prediction a priority in healthcare.
🗒️ Study
The study presents the MCODBC-HDDC method, which combines blockchain technology with deep learning techniques to enhance disease detection and classification. The model employs a systematic approach that includes data preprocessing through Z-score normalization and feature optimization using the Spotted Hyena Optimization Algorithm. The attention bidirectional gated recurrent unit (ABiGRU) method is utilized for effective disease classification, with hyperparameter tuning achieved through the modified coati optimization algorithm.
📈 Results
The experimental analysis of the MCODBC-HDDC approach demonstrated a superior accuracy of 97.36% when tested on the HD dataset. This performance surpasses existing models, highlighting the effectiveness of integrating blockchain with deep learning for healthcare applications. The results indicate a significant improvement in disease diagnosis accuracy, which is crucial for timely medical interventions.
🌍 Impact and Implications
The implications of this study are profound. By leveraging blockchain and deep learning, the MCODBC-HDDC method not only enhances the accuracy of disease detection but also ensures the privacy and security of patient data. This innovative approach could pave the way for more sustainable healthcare practices, ultimately leading to improved patient outcomes and more efficient healthcare systems. The potential for broader applications in various health-based sensing environments is immense, making this a significant breakthrough in the field.
🔮 Conclusion
The MCODBC-HDDC method represents a significant advancement in the integration of AI and blockchain technology in healthcare. With its impressive accuracy and focus on data privacy, this approach has the potential to transform disease detection and classification processes. As we continue to explore the capabilities of AI in healthcare, further research and development in this area could lead to even more innovative solutions for patient care. The future of healthcare looks promising with such advancements on the horizon!
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Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification.
Abstract
The growing number of patients and the emergence of new symptoms and diseases make health monitoring and assessment increasingly complex for medical staff and hospitals. The execution of big and heterogeneous data gathered by medical sensors and the necessity of patient classification and disease analysis have become serious problems for various health-based sensing applications. The significant features of healthcare are the privacy of medical details and the accuracy of disease identification. One of the key benefits of the healthcare system is the ability to predict diseases early. Recently, the progress of artificial intelligence (AI) in the healthcare system has been a high priority. Machine learning (ML) and deep learning (DL) effectively make analyses and strategic decisions for the healthcare system. This manuscript proposes a Modified Coati Optimization Driven Blockchain for Healthcare Disease Detection and Classification (MCODBC-HDDC) method. The presented MCOBC-HDDC method provides an efficient and accurate disease diagnosis, utilizing a system that depends on DL techniques. Initially, the MCODBC-HDDC method incorporates BC technology to ensure secure data sharing and management, providing a decentralized and tamper-proof environment for patient data. In the data preprocessing stage, the MCODBC-HDDC model employs Z-score normalization to standardize the data and improve performance. For the optimal subset of features, the spotted hyena optimization algorithm (SHOA) model is used. Furthermore, the attention bidirectional gated recurrent unit (ABiGRU) method is implemented for disease detection and classification. Finally, the hyperparameter selection of the ABiGRU method is performed by utilizing the modified coati optimization algorithm (MCOA) method. The experimental analysis of the MCODBC-HDDC approach is examined under the HD dataset. The performance validation of the MCODBC-HDDC approach portrayed a superior accuracy value of 97.36% over existing models.
Author: [‘Mohamed HG’, ‘Alrowais F’, ‘Al-Wesabi FN’, ‘Duhayyim MA’, ‘Hilal AM’, ‘Motwakel A’]
Journal: Sci Rep
Citation: Mohamed HG, et al. Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification. Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification. 2025; 15:21058. doi: 10.1038/s41598-025-06578-6