Blockchain-enabled digital twin system for brain stroke prediction
Abstract A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-024-00247-6 |
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| author | Venkatesh Upadrista Sajid Nazir Huaglory Tianfield |
| author_facet | Venkatesh Upadrista Sajid Nazir Huaglory Tianfield |
| author_sort | Venkatesh Upadrista |
| collection | DOAJ |
| description | Abstract A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes. |
| format | Article |
| id | doaj-art-47c96f3e1376477588b7c069194fae21 |
| institution | Kabale University |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-47c96f3e1376477588b7c069194fae212025-01-19T12:44:05ZengSpringerOpenBrain Informatics2198-40182198-40262025-01-0112111510.1186/s40708-024-00247-6Blockchain-enabled digital twin system for brain stroke predictionVenkatesh Upadrista0Sajid Nazir1Huaglory Tianfield2Department of Computing, Glasgow Caledonian UniversityDepartment of Computing, Glasgow Caledonian UniversityDepartment of Computing, Glasgow Caledonian UniversityAbstract A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes.https://doi.org/10.1186/s40708-024-00247-6Security and privacyMachine learningInternet of medical thingsScalabilityExtendibility |
| spellingShingle | Venkatesh Upadrista Sajid Nazir Huaglory Tianfield Blockchain-enabled digital twin system for brain stroke prediction Brain Informatics Security and privacy Machine learning Internet of medical things Scalability Extendibility |
| title | Blockchain-enabled digital twin system for brain stroke prediction |
| title_full | Blockchain-enabled digital twin system for brain stroke prediction |
| title_fullStr | Blockchain-enabled digital twin system for brain stroke prediction |
| title_full_unstemmed | Blockchain-enabled digital twin system for brain stroke prediction |
| title_short | Blockchain-enabled digital twin system for brain stroke prediction |
| title_sort | blockchain enabled digital twin system for brain stroke prediction |
| topic | Security and privacy Machine learning Internet of medical things Scalability Extendibility |
| url | https://doi.org/10.1186/s40708-024-00247-6 |
| work_keys_str_mv | AT venkateshupadrista blockchainenableddigitaltwinsystemforbrainstrokeprediction AT sajidnazir blockchainenableddigitaltwinsystemforbrainstrokeprediction AT huaglorytianfield blockchainenableddigitaltwinsystemforbrainstrokeprediction |