Enhancing Healthcare Diagnostics and Medical Data Security Using AI and Blockchain Technology Volume 57- Issue 5

Lela Mirtskhulava¹ and David Al-Dabass²*

• ¹Department of Computer Science, Ivane Javakhishvili Tbilisi State University, San Diego State University Georgia, Georgia
• ²School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

Received: July 19, 2024; Published: July 26, 2024

*Corresponding author: David Al-Dabass, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

DOI: 10.26717/BJSTR.2024.57.009068

Abstract PDF

Healthcare diagnostics have traditionally relied on methods that may not fully capture physiological data’s nonlinear and time-dependent nature. This review paper explores the applications of dynamical systems based on deep learning algorithms to model emergent characteristics in healthcare diagnostics. The study demonstrates how these advanced algorithms can significantly improve diagnostic accuracy and efficiency, offering new possibilities for early disease detection and patient care. On the other hand, the given paper reviews recent advancements in artificial neural networks (ANNs) and emerging technologies applied to stroke diagnosis and medical security. It examines key studies and covers applications of ANNs in mobile diagnostics, blood clotting prediction, brain wave signal recognition, and data security using multichain blockchain technology and postquantum signatures. These innovations made significant strides in improving diagnostic accuracy, patient outcomes, and data security in healthcare diagnostics.

Keywords: AI; Medical Data Security; Blockchain Technology; Healthcare Diagnostics

Abbreviations: AI: Artificial Intelligence; DL: Deep Learning; ANN: Artificial Neural Networks; EEG: Electroencephalography; BPQS: Post-Quantum Signatures; MIoT: Medical Internet of Things; ERPs: Event- Related Potentials; BCI: Brain-Computer Interface

The rapid advancement of artificial intelligence (AI), machine learning (ML), and deep learning (DL) has revolutionized various domains including healthcare. Traditional diagnostic methods often struggle with the complexity and dynamism of medical data. Artificial neural networks (ANNs) ability to process complex data and identify patterns makes them particularly useful in medical diagnostics. In the given review paper, we present the application of deep learning algorithms based on dynamical systems theory, focusing on their ability to model emergent characteristics in healthcare diagnostics. Healthcare diagnostics is a crucial field that stands to gain immensely from advancements in computer science, particularly in computational intelligence and deep learning [1-3], and brain-computer interface research [4].

Therefore, integrating mobile applications, blockchain technology, and AI for monitoring brain attacks and analyzing consciousness through brain wave measurements is discussed [5]. One promising area of research involves brain monitoring to diagnose deficiencies in biological intelligence functions, including electroencephalography (EEG) research and neurotechnology for enhancing brain-computer interfaces [6]. Emergent characteristics in medical data refer to properties and behaviors that arise from the interaction of simpler underlying elements, such as physiological signals. These characteristics are often nonlinear and vary over time, making them challenging to model with conventional techniques. Dynamical systems theory, which deals with the behavior of complex systems over time, provides a robust framework for understanding and predicting these patterns [7].

A cutting-edge approach to enhance healthcare diagnostics by integrating dynamical systems theory with deep learning algorithms is an innovative framework designed to model emergent characteristics in complex medical data, ultimately improving the accuracy and reliability of diagnostic predictions. The process begins with the meticulous collection and preprocessing of comprehensive medical datasets. These datasets include clinical data patient records, and time-series information about disease progression. Preprocessing steps are essential to handle missing values, normalize features, and transform the data into formats suitable for advanced analysis [8-10]. Next, the methodology applies dynamical systems theory to capture medical conditions’ intricate, time-dependent behaviors. By modeling the interactions and dependencies among various physiological variables over time, the authors use differential equations and state-space representations to describe the system’s dynamical behavior.

This theoretical framework provides a solid foundation for understanding how different factors influence disease progression. To enhance the modeling capabilities, deep learning algorithms, specifically recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are integrated. By combining deep learning with the dynamical systems framework, the model gains the ability to capture complex temporal dependencies and non-linear relationships inherent in medical data. A key aspect of the methodology is the focus on modeling emergent characteristics, such as disease onset, progression, and patient outcomes. The authors employ advanced techniques like long short-term memory (LSTM) networks to effectively capture long-term dependencies in the data. This enables the model to make accurate and meaningful predictions about future health events. The validation and testing phase is crucial to ensure the model’s robustness and generalizability. The authors use cross-validation techniques to validate the model’s performance and compare it against traditional diagnostic methods. Extensive testing on independent datasets demonstrates the model’s effectiveness and reliability in real- world scenarios.

The methodology outlined in the review article “Securing Medical Data in 5G and 6G via Multichain Blockchain Technology using Post-Quantum Signatures” focuses on enhancing the security of medical data in next-generation networks. This is achieved through an innovative integration of multichain blockchain technology with post-quantum digital signatures. To start, the methodology capitalizes on the fundamental properties of blockchain technology, including decentralization, transparency, and immutability, to ensure the security and integrity of data. By employing cryptographic hash functions and digital signatures, the authenticity of blocks and transactions is maintained. The introduction of multichain blockchains addresses critical concerns related to mining, openness, and privacy. This approach eliminates the need for proof-of-work mining, restricts the visibility of activities to authorized participants, and controls transaction permissions, thereby enhancing the overall security framework. A pivotal component of this methodology is the adoption of post-quantum digital signatures, specifically stateful hash-based signature schemes like XMSS and LMS, which have been approved by NIST. These signatures provide robust security against the emerging threats posed by quantum computing. The methodology introduces the Blockchained Post-Quantum Signatures (BPQS) scheme, which is tailored for blockchain applications, offering fast initial signatures and efficient key management. The proposed Medical IoT (MIoT) architecture is structured into three layers: device, gateway, and cloud.

This architecture leverages blockchain technology to ensure secure data transmission and storage within the medical Internet of Things ecosystem. The Mininet environment is utilized to test this architecture, demonstrating significant improvements in security and performance over traditional centralized systems. For implementation and testing, gateways are configured using virtual machines and the Ethereum blockchain within the Mininet environment. Security measurements conducted during testing show that the distributed architecture significantly outperforms its centralized counterpart in terms of security.

In the field of healthcare diagnostics, dynamical systems-based deep learning algorithms represent a significant advancement. These algorithms excel at uncovering complex and often nonlinear relationships within medical data. Unlike traditional methods, which struggle with evolving datasets, deep learning models adeptly discern hidden patterns critical for understanding disease progression and patient health. An essential benefit lies in their ability to autonomously learn from raw data, extracting nuanced features that may elude human observation. This capability is vital in healthcare, where subtle variations in imaging data, genetic profiles, or clinical records can provide crucial diagnostic insights. By utilizing deep learning’s hierarchical representations, healthcare providers gain deep insights into emerging characteristics and disease dynamics, facilitating more accurate and timely diagnoses. Furthermore, the scalability of deep learning accommodates the exponential growth of healthcare data, effectively processing large datasets that include diverse types of information— from high-resolution medical images to real-time patient sensor data. This scalability not only improves diagnostic accuracy but also supports the development of personalized treatment plans tailored to individual patient needs. Moreover, these algorithms demonstrate impressive adaptability, remaining robust in dynamic healthcare environments where data constantly evolves. They seamlessly integrate new information, continuously refining their predictive capabilities and enhancing clinical decision-making.

Deep learning consistently demonstrates superior performance across various healthcare applications, surpassing traditional methods in tasks such as image analysis, predictive modeling, and assessing patient risks. This enhanced performance not only streamlines diagnostic workflows but also raises the standard of patient care, ushering in an era of precision medicine. the adoption of dynamical systems-based deep learning algorithms represents a paradigm shift in healthcare diagnostics, offering significant advancements in our ability to understand, predict, and treat medical conditions with unprecedented accuracy and effectiveness.

The role of multichain blockchain technology is instrumental in ensuring the security and integrity of medical data. By leveraging its decentralized architecture and cryptographic techniques such as post-quantum signatures, multichain blockchain enhances data privacy, transparency, and resilience against cyber threats.” The Multichain platform facilitates the creation of private blockchains within and across organizations, addressing issues related to mining, transparency, and privacy. Multichain blockchains (MB) offer three primary features:

1. They enable secure mining without relying on proof-of-work.
2. They ensure that blockchain activities are visible only to specific participants involved in the blockchain.
3. They introduce controls over authorized transactions.
Private blockchains resolve scalability concerns by allowing participants to manage block sizes, creating a closed system that includes transactions relevant to participating entities. In a Multichain blockchain, each node generates a unique private key that remains undisclosed to other nodes. This feature restricts blockchain access to authorized users through a “handshaking” process:

1. Each node is identified by a public address within the permitted user list.
2. Nodes can verify the addresses of other nodes within their authorized list.
3. Nodes can exchange control messages.

Each node confirms its ownership by returning a signature of the control message, validating its possession of the corresponding private key linked to the presented public address.

The medical IoT architecture integrates blockchain-enabled home gateways responsible for secure data transmission among IoT devices. In the distributed ledger system, blockchain operates through a cloud layer. The architecture consists of three layers: the device layer where sensors and wearable medical devices collect patient data, the gateway layer which manages the data, and the cloud layer where all blockchain transactions are recorded and processed. To validate our approach, we implemented the architecture using the Mininet environment for IoT device simulation. Gateways were configured using virtual machines on a Linux server, with Ethereum blockchain integration. Additionally, Amazon Elastic Compute Cloud was utilized. We conducted a comparative study between our proposed distributed architecture and a centralized IoT architecture in terms of performance and security. Security assessments demonstrated that the distributed architecture outperforms centralized models in terms of security measures”.

In healthcare diagnostics, integrating EEGLAB offers substantial benefits, particularly in the analysis of EEG data. EEG signals provide essential insights into brain activity, crucial for diagnosing neurological disorders, monitoring brain function during surgeries, and understanding cognitive processes. EEGLAB, a robust MATLAB toolbox, supports comprehensive preprocessing, analysis, and visualization of EEG data. It facilitates tasks such as artifact removal, spectral analysis, extraction of event-related potentials (ERPs), and localization of brain sources. These functionalities are pivotal for extracting meaningful EEG features, which can then be combined with dynamical systems- based deep learning algorithms. By integrating EEGLAB with deep learning models, researchers can enhance diagnostic accuracy and reliability. Deep learning algorithms excel at identifying complex patterns and relationships within EEG data, aiding in the discovery of biomarkers for neurological conditions. This approach not only improves diagnostic precision but also enables personalized treatment strategies based on individual brain activity profiles. EEGLAB’s scalability enables analysis of large-scale EEG datasets, supporting population studies and longitudinal research. Its intuitive interface and extensive plugin support further enhance its utility in clinical and research environments, empowering healthcare professionals to make informed decisions using robust neurological data.

The brain, as the central control unit of the human body, governs our thoughts, speech, movements, and memory, regulating the functions of various organs. While a healthy brain operates efficiently and autonomously, brain disorders can lead to severe consequences. Brain-computer interfaces (BCIs) are classified into three main types: invasive, semi-invasive, and non-invasive. Invasive techniques involve surgically implanting devices into the brain to capture neural signals. Semi-invasive techniques require devices to be inserted into the skull but not the brain itself. Non-invasive techniques, on the other hand, utilize devices placed on the scalp to record brain activity, typically measured by electroencephalography (EEG). A brain-computer interface (BCI) is a device that allows users to interact with computers through brain activity, commonly recorded via EEG. EEG is a physiological method that records brain activities through electrodes placed on the scalp, widely preferred for its non-invasive nature. Although EEG-based interfaces are easy to use and do not require surgery, they suffer from poor spatial resolution and cannot capture high-frequency signals due to signal attenuation by the skull. After EEG signals are captured, they are processed to generate control signals interpretable by computers. This processing is challenging and crucial for developing high-quality BCIs. EEG recordings reflect the mental or physical state of the individual. The development and refinement of BCIs, especially through the processing of EEG signals, hold the potential to revolutionize how we interact with technology, offering new avenues for medical applications, neurorehabilitation, and enhanced human- computer interaction.

The integration of blockchain, deep learning, and Mobile IoT into healthcare diagnostics signifies a transformative shift toward more advanced and personalized medical practices: Blockchain Technology: Blockchain’s decentralized and immutable ledger capabilities enhance the security, privacy, and interoperability of medical data. It ensures secure and transparent data sharing among healthcare providers, improving patient privacy protection and data integrity. Blockchain’s role in healthcare extends to streamlining processes like medical record management, supply chain logistics, and patient consent management, ultimately fostering more efficient and patient-centric care delivery.

Deep Learning

Deep learning algorithms, particularly when integrated with dynamical systems approaches, revolutionize healthcare diagnostics by leveraging complex data relationships and patterns. These algorithms excel in tasks such as medical imaging analysis, predictive modeling of disease progression, and personalized treatment planning. By autonomously learning from vast datasets, deep learning enhances diagnostic accuracy, speeds up decision-making processes, and enables early detection of diseases, thereby improving patient outcomes and reducing healthcare costs.

Mobile IoT (MIoT)

MIoT devices, including wearables and sensors, play a crucial role in continuous health monitoring and data collection. These devices enable real-time tracking of vital signs, medication adherence, and patient activities, providing clinicians with comprehensive and timely patient insights. The integration of MIoT with blockchain ensures secure and seamless data transmission, while deep learning algorithms analyze this data to derive actionable insights. This interconnected ecosystem facilitates remote patient monitoring, early intervention in critical conditions, and personalized healthcare management tailored to individual patient needs.

The convergence of blockchain, deep learning, and Mobile IoT technologies in healthcare diagnostics represents a paradigm shift toward more efficient, accurate, and patient-centric healthcare delivery systems. These technologies not only enhance diagnostic capabilities but also empower healthcare providers to deliver proactive and personalized care, ultimately improving patient.

1. Mirtskhulava L, Wong J, Pearce G, Al Majeed S (2015) Artificial neural network model in stroke diagnosis. In Proceedings of the 2015 17th UKSIM-AMSS International Conference on Modelling and Simulation. Cambridge University, UK.
2. Pearce G, Mirtskhulava L, Wong J, Al Majeed S, Bakuria K, et al. (2015) Artificial neural network and mobile applications in medical diagnosis. In Proceedings of the 2015 17th UKSIM-AMSS International Conference on Modelling and Simulation. Cambridge University, UK.
3. Mirtskhulva L (2018) Featured Article: Monitoring Brain Attacks using AI Diagnosis. AIMed J.
4. Karam J, Al Majeed S, Yalung C N, Mirtskhulava L (2016) Neural network for recognition of brain wave signals. International Journal of Enhanced Research in Science, Technology & Engineering 5(10): 36-42.
5. Mirtskhulava L, Iavich M, Razmadze M, Gulua N (2021) Securing medical data in 5G and 6G via multichain blockchain technology using post-quantum signatures. In Proceedings of the 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo) Odesa, Ukraine, pp. 72-75.
6. Al Dabass D, Mirtskhulava L (2021) Chapter 1 - Application of dynamical systems based deep learning algorithms to model emergent characteristics for healthcare diagnostics. Elsevier, p. 1-29.
7. Al Dabass D, Evans D J, Sivayoganathan K (2002) Derivative abduction using a recurrent network architecture for parameter tracking algorithms. In IEEE 2002 Joint International Conference on Neural Networks. World Congress on Computational Intelligence.
8. Al Dabass D, Evans D J, Sivayoganathan K (2002) A recurrent network architecture for non-linear parameter tracking algorithms. In 2nd International Conference on Neural, Parallel, and Scientific Computations, Morehouse College, Atlanta, USA 2: 167-170.
9. Al Dabass D, Evans DJ, Sivayoganathan K (2003) Signal parameter tracking algorithms using hybrid recurrent networks. International Journal of Computational Mathematics 80(10): 1313-1322.
10. Al-Dabass D (2007) Chapter 12: Nature-Inspired Knowledge Mining Algorithms for Emergent Behaviour Discovery in Economic Models. In A. Zreiba (Edn.)., Parameter estimation algorithms for hierarchical distributed systems (pp. XXX-XXX). IGI Global.