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. 2023 Sep 7;23(18):7740.
doi: 10.3390/s23187740.

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

Aitizaz Ali  1 , Hashim Ali  2 , Aamir Saeed  3 , Aftab Ahmed Khan  4 , Ting Tin Tin  5 , Muhammad Assam  6 , Yazeed Yasin Ghadi  7 , Heba G Mohamed  8
Affiliations

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning

Aitizaz Ali et al. Sensors (Basel). .

Abstract

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Keywords: IoT; blockchain; data storage optimization; decentralized applications; health system and access; homomorphic encryption; lightweight authentication; permissions-based system; smart city.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic of the flowchart representing the proposed methodology.
Figure 2
Figure 2
Applications of Internet of Things.
Figure 3
Figure 3
Application of cloud computing.
Figure 4
Figure 4
System model representing the flow of massive IoT data.
Figure 5
Figure 5
Schematic representation of the proposed smart contract integration with cloud.
Figure 6
Figure 6
Schematic presentation of the proposed access control and outsourcing through Blockchain.
Figure 7
Figure 7
Data Flow through Proposed Network.
Figure 8
Figure 8
Timeline execution through Proposed Framework.
Figure 9
Figure 9
Simulations results based on the number of nodes versus the number of counts.
Figure 10
Figure 10
Classification of users based on the behavior and interaction with the system model.
Figure 11
Figure 11
Simulation results based on the number of sensors output with respect to number of nodes.
Figure 12
Figure 12
Comparative analysis of the proposed framework versus benchmark model [5,25,54] based on the speed and number of nodes.
Figure 13
Figure 13
Comparative analysis with the proposed framework versus benchmark model based on the latency and number of nodes [5,25,42,43,54].
Figure 14
Figure 14
Comparative analysis based on number of nodes versus encryption time [5,25,54].
Figure 15
Figure 15
Comparative analysis based on number of nodes versus encryption time [5,25,54].
Figure 16
Figure 16
Comparative analysis based on number of attributes and index search [5,25,54].
Figure 17
Figure 17
Comparative analysis based on classical optical power versus secret key rate [5,25,42,43,54].
Figure 18
Figure 18
Comparative analysis based on d2d distance versus number of transactions.
Figure 19
Figure 19
Schematic diagram representing the simulation results based on the number of attributes versus complexity.
Figure 20
Figure 20
Comparative analysis of the proposed approach versus benchmark models.
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