Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
Machine learning-based (ML) systems are becoming the primary means of achieving the highest levels of productivity and effectiveness. Incorporating other advanced technologies, such as the Internet of Things (IoT), or e-Health systems, has made ML the first choice to help automate systems and predic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/2/1035 |
| _version_ | 1827628897893613568 |
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| author | Abdulatif Alabdulatif Muneerah Al Asqah Tarek Moulahi Salah Zidi |
| author_facet | Abdulatif Alabdulatif Muneerah Al Asqah Tarek Moulahi Salah Zidi |
| author_sort | Abdulatif Alabdulatif |
| collection | DOAJ |
| description | Machine learning-based (ML) systems are becoming the primary means of achieving the highest levels of productivity and effectiveness. Incorporating other advanced technologies, such as the Internet of Things (IoT), or e-Health systems, has made ML the first choice to help automate systems and predict future events. The execution environment of ML is always presenting contrasting types of threats, such as adversarial poisoning of training datasets or model parameters manipulation. Blockchain technology is known as a decentralized network of blocks that symbolizes means of protecting block content integrity and ensuring secure execution of operations.Existing studies partially incorporated Blockchain into the learning process. This paper proposes a more extensive secure way to protect the decision process of the learning model. Using smart contracts, this study executed the model’s decision by the reversal engineering of the learning model’s decision function from the extracted learning parameters. We deploy Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers decision functions on-chain for more comprehensive integration of Blockchain. The effectiveness of this proposed approach is measured by applying a case study of medical records. In a safe environment, SVM prediction scores were found to be higher than MLP. However, MLP had higher time efficiency. |
| first_indexed | 2024-03-09T13:42:08Z |
| format | Article |
| id | doaj.art-3a4d7e176ccb4319aabc8ef47dbec59f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:42:08Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-3a4d7e176ccb4319aabc8ef47dbec59f2023-11-30T21:05:15ZengMDPI AGApplied Sciences2076-34172023-01-01132103510.3390/app13021035Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making FrameworkAbdulatif Alabdulatif0Muneerah Al Asqah1Tarek Moulahi2Salah Zidi3Department of Computer Science, College of Computer, Qassim University, Buraidah 52571, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraidah 52571, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraidah 52571, Saudi ArabiaISSIG, University of Gabes, Gabes 6072, TunisiaMachine learning-based (ML) systems are becoming the primary means of achieving the highest levels of productivity and effectiveness. Incorporating other advanced technologies, such as the Internet of Things (IoT), or e-Health systems, has made ML the first choice to help automate systems and predict future events. The execution environment of ML is always presenting contrasting types of threats, such as adversarial poisoning of training datasets or model parameters manipulation. Blockchain technology is known as a decentralized network of blocks that symbolizes means of protecting block content integrity and ensuring secure execution of operations.Existing studies partially incorporated Blockchain into the learning process. This paper proposes a more extensive secure way to protect the decision process of the learning model. Using smart contracts, this study executed the model’s decision by the reversal engineering of the learning model’s decision function from the extracted learning parameters. We deploy Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers decision functions on-chain for more comprehensive integration of Blockchain. The effectiveness of this proposed approach is measured by applying a case study of medical records. In a safe environment, SVM prediction scores were found to be higher than MLP. However, MLP had higher time efficiency.https://www.mdpi.com/2076-3417/13/2/1035blockchaine-healthmachine learningdeep learningsmart contractdecision function |
| spellingShingle | Abdulatif Alabdulatif Muneerah Al Asqah Tarek Moulahi Salah Zidi Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework Applied Sciences blockchain e-health machine learning deep learning smart contract decision function |
| title | Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework |
| title_full | Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework |
| title_fullStr | Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework |
| title_full_unstemmed | Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework |
| title_short | Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework |
| title_sort | leveraging artificial intelligence in blockchain based e health for safer decision making framework |
| topic | blockchain e-health machine learning deep learning smart contract decision function |
| url | https://www.mdpi.com/2076-3417/13/2/1035 |
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