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Questions and Answers
The absence of data on comorbid conditions may limit the accuracy of the model's predictions.
The absence of data on comorbid conditions may limit the accuracy of the model's predictions.
True (A)
The model utilizes a distributed architecture to enhance data security.
The model utilizes a distributed architecture to enhance data security.
True (A)
The model's reliance on static snapshots contributes to its high accuracy in situations requiring progressive data.
The model's reliance on static snapshots contributes to its high accuracy in situations requiring progressive data.
False (B)
The digital twin application software incorporates two user interfaces designed using ReactJS.
The digital twin application software incorporates two user interfaces designed using ReactJS.
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The DTSmartContractController stores data exclusively on a Ganache blockchain.
The DTSmartContractController stores data exclusively on a Ganache blockchain.
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Scikit-learn is the most commonly used Python library for machine learning.
Scikit-learn is the most commonly used Python library for machine learning.
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The author's approach includes the use of Random Forest for feature selection.
The author's approach includes the use of Random Forest for feature selection.
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The author's approach applies Batch Gradient Descent for model optimization.
The author's approach applies Batch Gradient Descent for model optimization.
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The author's approach involves using K-Nearest Neighbors as a primary machine learning algorithm.
The author's approach involves using K-Nearest Neighbors as a primary machine learning algorithm.
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The author's approach utilizes a technique called Univariate Feature Selection.
The author's approach utilizes a technique called Univariate Feature Selection.
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The Digital Twin application used a server with $100$ GHz of networking bandwidth to connect with heart monitoring devices.
The Digital Twin application used a server with $100$ GHz of networking bandwidth to connect with heart monitoring devices.
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The Digital Twin application used Solidity version $0.8.11$ for smart contract development.
The Digital Twin application used Solidity version $0.8.11$ for smart contract development.
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The Digital Twin application used ReactJS 17.0.2 to build user interfaces.
The Digital Twin application used ReactJS 17.0.2 to build user interfaces.
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The Digital Twin application used Scikit-learn version $0.24.1$ for machine learning.
The Digital Twin application used Scikit-learn version $0.24.1$ for machine learning.
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The Digital Twin application used PostgreSQL version $9.9$ as a relational database management system.
The Digital Twin application used PostgreSQL version $9.9$ as a relational database management system.
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Study Notes
Blockchain-enabled digital twin system for brain stroke prediction
- A digital twin is a virtual model of a real-world system, updating in real-time
- Digital twins are gaining popularity in healthcare for monitoring activities like diet, physical activity, and sleep
- Current research shows limited accuracy in predicting serious conditions like heart attacks, brain strokes, and cancers using digital twins
- Data security and privacy issues continue to hinder widespread adoption of digital twin models
- A secure, machine learning-powered digital twin application was developed to enhance prediction accuracy, strengthen security, and ensure scalability
- The application demonstrated 98.28% accuracy for brain stroke prediction on a selected dataset
- Consortium blockchain technology was integrated with machine learning for enhanced data security, creating a tamper-proof system
- Data anomalies are automatically corrected to maintain robust data protection
- The application can be adapted to monitor other pathologies like heart attacks, cancers, osteoporosis, and epilepsy with minimal adjustments
Introduction
- A digital twin is a digital representation of a physical entity (e.g., person or machine)
- The industrial and academic research communities have significantly advanced the use of digital twins in various applications, including industrial processes, aerospace, and healthcare
- In healthcare, digital twins are utilized in hospitals to analyze operational changes, evaluate staffing levels, and optimize care delivery
- Remote patient monitoring leverages the benefits of digital twins
Literature Review
- Digital twin frameworks, which virtually replicate physical systems using patient data and advanced computational techniques, have shown promise in predicting pathologies like brain stroke
- Several studies have explored the application of digital twin technology, striving to enhance stroke prediction accuracy and improve patient outcomes
- Key studies examined in the literature review include those proposing digital twin frameworks that leverage machine learning algorithms for prediction accuracy (accuracy levels varied)
- Concerns have been voiced regarding the lack of security and privacy measures in several prior studies.
Methods
- A machine learning (ML) based Digital Twin application for predicting brain strokes was developed
- The application leverages a public dataset from Kaggle (4,981 records)
- The dataset includes patient data, such as age, sex, existing medical history, and lifestyle factors
- Three files (Patient EHR, supplementary data, and transactional data) were created to simulate patient data
- The data in these files was simulated, but drew some information from the Kaggle dataset
- Data was stored in XML format, for integration with wearable devices
- Data from wearables and other sources is stored in a CSV file
- Medical device information, combined with clinical history, is stored on a blockchain
Results
- A digital twin application was developed with an accuracy exceeding 98.28% for stroke prediction
- The application was designed to adapt to other diseases (heart attacks, cancers, osteoporosis, and epilepsy) with minimal code changes
- The system successfully detected deliberate data tampering, demonstrating the security benefits of using blockchain in healthcare
Discussion
- Security and privacy are crucial concerns in healthcare, potentially impacting the implementation of digital twin solutions
- This model leverages a consortium blockchain for robust data security
- The application’s scalability makes it adaptable to various critical illnesses with minimal code adjustments
- The model's accuracy (98.28%) exceeds those in previous studies (generally ranging from 84–92%)
Conclusion
- A digital twin application was successfully developed to predict stroke
- The model demonstrates capabilities for prediction, scalability, and security
- Further research is proposed to evaluate the model across more diverse and real-world datasets
- Adaptability for other pathologies is demonstrated
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Description
Explore the innovative application of blockchain and digital twin technology in predicting brain strokes. This quiz discusses the integration of secure machine learning and consortium blockchain to enhance data accuracy and security in healthcare. Learn about the challenges and advancements in using digital twins for predicting serious health conditions.
