E-healthcare System Research Articles

E-Healthcare System for Stroke Management Using IoT and Machine Learning

Machine learning is one of the hottest topics in e-healthcare and has a big social impact. It is challenging to build and develop an efficient and successful e-healthcare system without machine learning. Machine learning's primary function is to analyse healthcare data, which is gathered from various sources and can be both homogeneous and heterogeneous. Therefore, creating an effective machine learning algorithm that functions with both homogeneous and heterogeneous data is a difficult task. On the other hand, it is noted that a sizable number of health-related products, such as fitness bands, smart watches, and sensors, are being developed, and the majority of people use these items to track their own health. Each person's health data is also gathered by these devices, and a machine learning algorithm is integrated into the data to identify any strange patterns or behaviours. These gadgets capture any aberrant behaviour or pattern, and an alarm message is sent to the individual. Thus, machine learning (ML) presents a promising technique for cutting healthcare device costs and describing doctor-patient relationships at a higher level.

Evaluating the Sustainable Competitive Advantage of the Saudi e-Healthcare System: An Empirical Study [Letter

Evaluating the Sustainable Competitive Advantage of the Saudi e-Healthcare System: An Empirical Study [Letter

A secure and privacy preserving model for healthcare applications based on blockchain-layered architecture

Blockchain is one of the emerging technologies that are applied in various fields and its application in Healthcare 4.0 is crucial to handle the vast amount of health records that are growing continuously everyday. This paper explores the application of blockchain technology-based layered architecture integrated with Interplanetary File System (IPFS) storage to provide a secure and robust sharing platform for healthcare system. A multilayer security is provided in the proposed system including a 128-bit Random Deoxyribonucleic Acid (DNA) encoding sequence with a modified version of the 128-bit Advanced Encryption Standard (AES) key expansion symmetric key technique is being used before storing the records in the IPFS storage, Rivest–Shamir–Adleman (RSA) 2048 digital envelope is used for secure transmission of symmetric keys, RSA 1024 is used for digital signature, and Secure Hash Algorithm 256-bit (SHA-256) is used for hashing the record files. Comparative analysis with existing state-of-the-art in blockchain-based E-healthcare system is discussed. The performance of various assessment metrics is evaluated, including encryption and decryption time for EHR records, mining time, record retrieval time, smart contract execution time and network latency. Additionally, a reputation-based filtering mechanism specifically designed in the proposed model to detect and mitigate the various attacks.

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Secure Blockchain Assisted Attribute-Based Keyword Search for Collaborative E-Healthcare

In the age of technological advancement, collaborative E-healthcare emerges as a transformative system eliminating traditional location and accessibility barriers in healthcare services. Here, Searchable Encryption (SE) plays a key role in enabling healthcare providers to outsource encrypted medical data and search services to third parties like cloud servers, thereby reducing storage and management expenses. This intermediary approach poses challenges of single-point failure, privacy breaches, and potentially untrustworthy results. State-of-the-art public key-based SE methods use a cloud-assisted architecture that doesn’t support reliable and practical searches with fine-grained permissions. Also, such systems require additional support to address potential privacy leakages and ensure data availability at the storage server. To address these concerns, we propose a B lockchain-assisted E fficient and S ecure K eyword S earch (BESKS) scheme to enforce fine-grained keyword search privilege control while achieving practical search complexity. Our scheme employs a ciphertext policy attribute-based keyword search mechanism where keywords are encrypted using expressive access policies to build an inverted index structure. The encrypted indexes are stored on the blockchain while encrypted medical documents are stored on InterPlanetary File System (IPFS) nodes to enhance availability and ensure the reliability and scalability of our approach. Our scheme utilizes blockchain-based smart contract for efficient, secure search operations and ensures financial fairness in fine-grained searches. Search tokens are generated based on user attributes and query keywords to facilitate private searches on-chain. To enhance the search process, our secure index enables exact match for a query keyword in constant time to ensure expensive authorization operations are performed only once. Theoretical analysis suggests that our BESKS is more efficient and secure than state-of-the-art schemes. Prototype implementation results on the Ethereum blockchain network further validate its feasibility for real-world applications, demonstrating the scheme's practical applicability in collaborative E-Healthcare systems.

Designing and implementing a resilient immutability mechanism for enhanced supply chain management in E-healthcare systems

Counterfeit drugs pose significant health risks due to their variable efficacy and potential harmful ingredients. To combat this issue, a reliable and secure track-and-trace system is essential for pharmaceutical supply chains. This paper proposes an Immutable and Decentralized Pharma (IDP) model, leveraging blockchain technology to ensure the safe and efficient distribution of medications. The IDP model utilizes smart contracts to record transactions between entities onto a blockchain, enabling end-to-end product tracking and provenance. Experimental results on a polygon blockchain test network demonstrate the feasibility and enhanced security of the IDP model in a collaborative environment. Our solution addresses the challenges of data privacy, openness, and authenticity inherent in centralized track-and-trace systems, providing a promising approach to eliminate counterfeits and guarantee product safety in pharmaceutical supply chains.

Clinical Prediction using Machine Learning-based IoT for E-Healthcare Systems

Machine learning is one of the fundamental approaches to nature the data produced by Internet of Things (IoT) and has drastic enhancements to enhance the decision-making fields such as education, security, business, and especially the healthcare sector. This integration of ML with IoT makes it possible for healthcare systems to generate patterns from big data which can improve the efficiency of predictive analytics and referrals. It also helps in the development of templates for medical records and real-time patient care, and the identification of patient diagnosis electronically. Though, performance of the ML algorithms can be affected by the dataset on the basis of which clinical decisions are made. Such variations clearly demonstrate the requirement to gain a deeper understanding of how several ML approaches work in terms of IoT data analysis for healthcare. In the proposed study we are testing the Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms for tracking health parameters. We first employ the above-mentioned approach for training and validation of the ML models on the database available at UCI. In the testing phase, the IoT devices record actual data of heart rate, blood pressure, posture, and temperature to forecast a health anomaly. Subsequently, an examination of accuracy of the aforementioned prediction is performed on the cloud stored data with reference to those produced by K-nearest neighbour (KNN) algorithm. Such an approach makes it possible to give adequate consideration to the offered assessment of the role of ML in increasing the efficiency of IoT-based healthcare systems with regard to early detection and comprehensive intervention practices. In this way, this research help to consider variability in prognostic outcomes for more accurate and precise clinical decision-making processes, and thus contribute in enhancing patient care and health in IoT environment.

A Secure Data E-Governance for Healthcare Application in Cyber Physical Systems

The bio-medical devices gather patient information and communicate it to data consumers via wireless networks to take the appropriate action and decision by informing the doctors. However, IoMT is adopted by healthcare departments with a greater speed, yet the majority of devices are limited to resource constraints and security perspectives. The classical e-healthcare systems that are centric have the inherent problem of single-point failure with low transparency and low control over records. Many proposals have been validated in IoT for addressing the inadequate computing and storage of records through sensors. The main focus of this paper is to propose a novel hybrid architecture called Zero Trust Blockchain Architecture for decentralized E-health-CPS systems to support low latency along with storage and processing of records while monitoring the patients. In addition, a probability distribution function may further draft an accurate and real-time monitoring of patients. The proposed mechanism is analyzed against adequate decision, storage, accuracy and transmission of records.

Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security

In the modern world, due to changing lifestyles, more people are infected by deadly diseases, especially heart disorders. An early prognosis is the only possible way of increasing survival rate. One of the key elements of this context is the healthcare digitalization that is carried out through Internet of Things (IoTs) and cloud computing. However, main issue in cloud with IoT is false diagnosis, which leads to cause a major impact on patient’s life. Moreover, data communication through cloud servers can be compromised by attackers due to security issues. Therefore, to overcome these issues, this paper proposes a novel secure e-healthcare system with dual objectives- accurate disease prediction and improved cloud security. A novel diagnosis approach ‘Hybrid Binary Particle Firefly Optimized Extreme Learning Machine classifier (HybBPF-ELM)’ to predict HD is designed, that recognize the subject with HD abnormality from the normal ones. The prediction accuracy and time efficiency of training process are improved by the adoption of a Hybrid Binary Particle Firefly Optimizer (HBPFO) through fine-tuning weight and bias of Extreme Learning Machine (ELM) classifier. Additionally, a new intelligent encryption and decryption framework ‘IEDF’ is introduced for cloud security. It combines four encryption algorithms, including Advanced Encryption Standard (AES), Data Encryption Standard (DES), Rivest- Shamir-Adleman (RSA), and Modified Blow Fish (MBF) to enhance cryptographic strength and key security. Along with this, an Automatic Sequence Encryption (ASC) for data block encryption is employed to ensure strong security and network performance. The integration of HD prediction module and cloud security framework within e-healthcare system assists healthcare experts in securely storing and transferring data for advanced diagnosis. The results show that our proposed system achieves an overall prediction accuracy of 99.36 % and less processing time for encryption and decryption of 127.55 s and 452.01 s at 2.2 GB file size respectively.

An Authentic-based Privacy Preservation System for Smart E-Healthcare Systems Based on Zero Run Length Encoding and DNA Cryptography

An Authentic-based Privacy Preservation System for Smart E-Healthcare Systems Based on Zero Run Length Encoding and DNA Cryptography

A robust medical image cryptosystem based on hyperbolic chaotic system and circular bit-shifting-flipping techniques

Due to the hasty growth of digital technology, securing medical images for transmission and storage is a significant issue. Information on the texture of medical images is essential for diagnosis. Hence, protecting these content has become a great challenge in the e-Healthcare system. So, we propose a novel dynamic circular bit shifting and bit-flipping process in the diffusion phase of the image cryptosystem to provide high level protection for the medical image. In the developed framework, at first, the hyperbolic chaotic map is executed for generating three hyper chaotic series and these chaotic series are utilized for encryption process. In encryption process, primarily the secret image is confused by utilizing any two chaotic series, then the confused image is divided into blocks and each block is diffused by executing the proposed dynamic circular bit shifting and bit-flipping process which greatly increases the probability and complexity level of the developed cryptosystem. Evaluation using different medical images represents that the proposed model has robustness, high efficiency, key sensitivity and also has the ability to withstand different attacks. This has been represented using the results of security measures and statistical analysis, such as Shannon entropy (7.997), CC (< 0), NPCR (99.63 %), UACI (37.14 %), SSIM (< 0), FSIM (< 0.5), PSNR (6.88). These results prove that the developed medical crypt can efficiently provide better security for medical images utilized in real time medical application like e-Healthcare and telemedicine.

DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system

This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model’s performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model’s decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.

Flexible and Fine-Grained Access Control for EHR in Blockchain-Assisted E-Healthcare Systems

Flexible and Fine-Grained Access Control for EHR in Blockchain-Assisted E-Healthcare Systems

A Framework for Secure Data Access and Searchability for E-Healthcare System

A Framework for Secure Data Access and Searchability for E-Healthcare System

Novel substitution-box generation using group theory for secure medical image encryption in E-healthcare

&lt;abstract&gt; &lt;p&gt;With the increasing need for secure transmission and storage of medical images, the development of robust encryption algorithms is of paramount importance. Securing sensitive digital medical imagery information during transmission has emerged as a critical priority in the e-Healthcare systems. Recent research has highlighted the significance of developing advanced medical image encryption algorithms to ensure secure transmission during telediagnosis and teleconsultations. In this study, we propose a novel medical image encryption algorithm which is based on a novel substitution-box generation algebraic method using a combination of a multiplicative cyclic group with an order of 256 and a permutation group with a large order. To evaluate the security performance of the proposed generated S-box, various standard security indicators are assessed and analyzed. The newly proposed medical image encryption algorithm utilizes the generated S-box, along with bit-plane slicing, circular shifting, and XOR operations, to achieve enhanced security and robustness for encrypting sensitive imagery data. In order to assess the effectiveness of the proposed encryption algorithm, a comprehensive benchmarking analyses, specifically designed for evaluating image encryption schemes, have been conducted. The results obtained from the comparison and other analyses serve to validate the optimal features and high cryptographic strength exhibited by the proposed method. Hence, the proposed algorithm demonstrates significant effectiveness and holds considerable promise in the realm of medical image encryption for secure e-Healthcare systems.&lt;/p&gt; &lt;/abstract&gt;

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

LSP–eHS: A Lightweight and Secure Protocol for e-Healthcare System

LSP–eHS: A Lightweight and Secure Protocol for e-Healthcare System

RSHealth: A Ring Signature Scheme for Identity Anonymization and Transaction Privacy in Blockchain Based E-Healthcare Systems

RSHealth: A Ring Signature Scheme for Identity Anonymization and Transaction Privacy in Blockchain Based E-Healthcare Systems

A trusted and adaptive security mechanism for wearable e-healthcare systems

A trusted and adaptive security mechanism for wearable e-healthcare systems

A Blockchain-Based E-Healthcare System With Provenance Awareness

A Blockchain-Based E-Healthcare System With Provenance Awareness