Smart Health Systems Research Articles

Resource optimization in smart electronic health systems using IoT for heart disease prediction via feedforward neural networks

Resource optimization in smart electronic health systems using IoT for heart disease prediction via feedforward neural networks

A Novel Cloud-Enabled Cyber Threat Hunting Platform for Evaluating the Cyber Risks Associated with Smart Health Ecosystems

The fast proliferation of Internet of Things (IoT) devices has dramatically altered healthcare, increasing the efficiency and efficacy of smart health ecosystems. However, this expansion has created substantial security risks, as cybercriminals increasingly target IoT devices in order to exploit their weaknesses and relay critical health information. The rising threat landscape poses serious concerns across various domains within healthcare, where the protection of patient information and the integrity of medical devices are paramount. Smart health systems, while offering numerous benefits, are particularly vulnerable to cyber-attacks due to the integration of IoT devices and the vast amounts of data they generate. Healthcare providers, although unable to control the actions of cyber adversaries, can take proactive steps to secure their systems by adopting robust cybersecurity measures, such as strong user authentication, regular system updates, and the implementation of advanced security technologies. This research introduces a groundbreaking approach to addressing the cybersecurity challenges in smart health ecosystems through the deployment of a novel cloud-enabled cyber threat-hunting platform. This platform leverages deception technology, which involves creating decoys, traps, and false information to divert cybercriminals away from legitimate health data and systems. By using this innovative approach, the platform assesses the cyber risks associated with smart health systems, offering actionable recommendations to healthcare stakeholders on how to minimize cyber risks and enhance the security posture of IoT-enabled healthcare solutions. Overall, this pioneering research represents a significant advancement in safeguarding the increasingly interconnected world of smart health ecosystems, providing a promising strategy for defending against the escalating cyber threats faced by the healthcare industry.

Anonymous Attribute-Based Searchable Encryption for Smart Health System

Anonymous Attribute-Based Searchable Encryption for Smart Health System

Advanced Denoising and Meta-Learning Techniques for Enhancing Smart Health Monitoring Using Wearable Sensors

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Retraction: ManikamBabu, ThangarajuJesudas. An artificial intelligence‐based smart health system for biological cognitive detection based on wireless telecommunication. Comput Intell38:1365–1378, 2022 (10.1111/coin.12513)

The above article, published online on 08 March 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest‐edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

Internet of Things Based-Early Diagnosis System of Diabetes Using Optimized Neural Network

One of the most common diseases today is diabetes. This disease has an initial phase, if it is diagnosed on time, it helps to control and treat this disease to a great extent. But early diagnosis of this disease is very challenging due to the difficulty of repeated tests, especially in some geographical areas. Therefore, in smart health systems, a series of information is frequently taken from users by mobile phone, and by analyzing this information using artificial intelligence and machine learning algorithms and with the help of the Internet of Things, the doctor can diagnose diabetes in the first steps remotely. In this paper, we used a database that has 16 features related to early diabetes. Our proposed method for early diagnosis of diabetes is based on feature ranking along with MLP neural network optimized with whale optimization algorithm (WOA). also, the NCA algorithm is used to rank the features, and the WOA algorithm is used to optimize the parameters of the MLP neural network. Finally, based on the simulation results of the proposed method, we achieved 98.1% accuracy in the early diagnosis of diabetes.

MED-TECH CONNECT

Med-Tech Connect also termed as Smart Health System is an organized computerized system designed and programmed to deal with day-to-day operations and management of hospital activities. The software system is designed to manage inpatients and outpatients, maintain records, handle treatment databases, track illness status, process pharmacy billings, and manage lab operations, ensuring efficient hospital management. Currently, a significant challenge faced by patients is accessing their medical reports post-consultation. Although many hospitals maintain these reports within their systems, patients often encounter difficulty retrieving them while outside the hospital premises. In addition, the system maintains patient data in a structured format. As part of this project, we aim to enhance functionality by enabling storage of reports in the database, facilitating access from any location worldwide. Keywords: Machine Learning, Database management, Web UI, Modules.

Deep learning enabled blockchain based electronic heathcare data attack detection for smart health systems

The idea of networked personal medical devices is a component of contemporary Smart Health Systems (SHS). These gadgets offer remote observing and the exchange of wellbeing information, which enormously further develop the patient's personal satisfaction while at the same time reducing treatment expenses for both the patient and the medical care suppliers (telemedicine). For individuals' wellbeing, a cutting edge individual wellbeing record framework is fundamental. Here, there are still difficulties with information mix from different EHRs, information interoperability, and guaranteeing that admittance to information is totally under the power of the patient. Some security issues are caused by the Network. To settle these issues, we propose a novel profound learning-based framework that circuits state of the art decentralized innovations like IPFS and blockchain with wellbeing information interoperability principles and advances like FHIR's APIs. In this review, we show that correspondence between private clinical gadgets is as a matter of fact powerless to different cyber attacks. We show how an outer assailant could involve man-in-the-center, replay, bogus information infusion, and refusal of-administration assaults to block delicate wellbeing information stream by capturing the correspondence of the individual clinical gadget. We likewise suggest an Interruption Recognition Framework (IDS), GAN, to additional screen traffic on private clinical gear and spot attacks against them. Our extensive investigation shows that GAN, with an F1-score of 98 % and an accuracy of 98.7 %, can successfully and accurately recognise numerous assaults on personal medical equipment.

Verifying trust over IoT-ad hoc network-based applications under uncertainty

The rapid integration of the Internet of Things (IoT) with ad hoc networks offers significant advantages for revolutionizing smart environments. However, ensuring trust and reliability within these interconnected systems remains a significant challenge. To address this challenge, this paper introduces an innovative three-valued (3v) trust model customized specifically for IoT-ad hoc systems. The model employs three-valued logic to evaluate trust in social commitments within ambiguous scenarios, where commitments serve as the foundation of the business logic. Our aim is to enhance the effectiveness and economic feasibility of IoT in smart environments. To advance this initiative, we introduce the 3v-TCTLC, a distinctive modeling language that extends the conventional two-valued logic by introducing a third value to accommodate uncertainty. This novel approach facilitates the assessment of trust in commitments within uncertain IoT-ad hoc environments. Additionally, we improve the functionality of the “MACMAS-interactor” tool, incorporating new features to support our 3v-TCTLC logic. Through two case studies in the domains of smart health monitoring and smart home systems, we validate our model against specific requirements in uncertain contexts. These case studies highlight the robustness and practicality of our proposed tools and methodologies. Compared to prevalent trust management strategies employed in IoT and ad-hoc networks, our methodology stands out distinctly. While many current solutions propose trust-centered protocols, with some even harnessing advanced technologies, they frequently overlook the crucial element of model checking. Our approach not only incorporates this critical component but also ensures the integrity of the system. Furthermore, even though the field of multi-valued model checking has seen advancements like χCTL, our research contributes significantly by integrating trust and commitment verification specifically designed for IoT-ad hoc environments. Our empirical assessments in the domains of smart health and home systems confirm that our tool and strategies demonstrate superior performance in terms of time and space usage and better adaptability and scalability in uncertain scenarios, representing a noteworthy advancement over existing techniques and tools.

Topological Data Analysis for Software Test Cases Generation

The escalating development of digital technologies over the last several decades has given rise to an accompanying surge in data analysis.The software based smart applications increase the importance of behaviour analysis. Software testing of expert systems such as electronic health records, health information system and software as a medical device (SaMD) refers to detect the difference between expected behaviour and actual outcome during healthcare expert systems development. Test cases are the core source of effective software testing. Test cases generation for expert systems are discover challenges of identifying the expected behaviour of the system, where the decision logic is obtained via a data –driven paradigm. In the traditional system software object oriented expected behaviour, provide the clear test cases does not change the flow such as in the healthcare system. Intelligent software test case generation approach required for smart health systems. In this research contributes key performance indicators from massive data sets, node –link diagrams from decision trees, test cases from adjacency matrix are elaborated. The experimental results of healthcare expert systems provide empirically evidence that topological data analysis are compact contribution for software requirement validation.

Microservice Deployment Mechanism With Diversified QoS Requirements for Smart Health System in Industry 5.0

Microservice Deployment Mechanism With Diversified QoS Requirements for Smart Health System in Industry 5.0

IRS-Driven Cybersecurity of Healthcare Cyber Physical Systems

Intelligent reflective surface (IRS) as one of the most potential 6G key technologies injects new vitality into the smart medical and health system by intelligently configuring the wireless communication environment and spawns the construction of a modern medical information system. Medical data security is a sensitive issue that hospitals are generally concerned about in recent years. There are great hidden dangers to information security in the environment of “cloud storage”. The IRS provides a new solution for enhancing the privacy and security of “cloud storage” medical file data, assisting in the efficient transmission and interconnection of information, and ensuring highly secure and uninterrupted services in healthcare-based cyber physical systems (CPS). Specifically, the modelling in this paper considers the situation where communication over multiple-input single-output (MISO) channel is eavesdropped by a patient spy holding a single antenna receiver, and additionally the patient spy is obscured by an obstacle. One of the multi-antenna base stations (BS) transmits independent data streams to doctors and keeps them secret from patient spies. By jointly optimizing the beamforming at the BS and the phase shift of the IRS reflection elements, the simulation analysis verifies that the introduction of IRS can effectively improve the privacy security of medical data.

Collusion resistant multi-authority access control scheme with privacy protection for personal health records

The personal health record (PHR) is an important part of the smart health system. The medical profession can provide medical services to patients in time by accessing PHRs stored in the cloud. However, PHR suffers from unauthorized access and disclosure of a large amount of private information at present. Privacy protection of personal data and tracking and accountability of malicious users have been important issues in smart health systems. In this paper, we construct an accountable access control scheme based on ciphertext-policy attribute-based encryption (CP-ABE) scheme. Blockchain accounting node distributes the decryption key, and an audit mechanism judges whether the key is leaked by a malicious user or a corrupt node. The authority and user jointly generate the complete decryption key, which reduces the power of authority and resists collusion attacks between revoked users and unrevoked users. The revocation mechanism deletes the malicious users by updating parts of the ciphertext. Furthermore, blockchain records all operations in the form of transactions that can prevent tampering with ciphertext and trace the dishonest behavior of each entity. Security analysis and performance efficiency comparison show that our scheme is more practical and has important practical significance than the existing schemes.

IoT-based patient stretcher movement simulation in smart hospital using type-2 fuzzy sets systems

The healthcare facilities in the modern age are the key challenge in developing countries where remote areas lack immense-quality hospitals and medical experts. The idea here is to provide an intelligent smart health system using some sensors and fuzzy controllers which are implemented in stretchers, and the update of the patient stretcher monitoring is implemented to monitor the immovable stretchers and rectify the problem of immovable stretchers, which can be solved by Type-2 fuzzy sets and systems, which is featured by three-dimensional characteristics. This study proposes IoT-based Patient Stretcher Movement Simulation for Smart Hospitals using Type-2 fuzzy sets and systems. Type-2 fuzzy logic is a generalisation of conventional fuzzy logic in which the stretcher movement simulation and exact location of the stretcher are monitored by the sensor-based fuzzy set and let the model minimise the effects of uncertainties which is found to have very wide applicability in rule-based fuzzy logic systems. The results presented in this paper prove the concept and provide the IoT-based patient stretcher creation. The results of the experiments also show that the proposed approach is more reliable and intelligent enough to provide efficient monitoring applications through the type-2 fuzzy logic systems.

Automatic Clustering for Improved Radio Environment Maps in Distributed Applications

Wireless communication greatly contributes to the evolution of new technologies, such as the Internet of Things (IoT) and edge computing. The new generation networks, including 5G and 6G, provide several connectivity advantages for multiple applications, such as smart health systems and smart cities. Adopting wireless communication technologies in these applications is still challenging due to factors such as mobility and heterogeneity. Predicting accurate radio environment maps (REMs) is essential to facilitate connectivity and improve resource utilization. The construction of accurate REMs through the prediction of reference signal received power (RSRP) can be useful in densely distributed applications, such as smart cities. However, predicting an accurate RSRP in the applications can be complex due to intervention and mobility aspects. Given the fact that the propagation environments can be different in a specific area of interest, the estimation of a common path loss exponent for the entire area produces errors in the constructed REM. Hence, it is necessary to use automatic clustering to distinguish between different environments by grouping locations that exhibit similar propagation characteristics. This leads to better prediction of the propagation characteristics of other locations within the same cluster. Therefore, in this work, we propose using the Kriging technique, in conjunction with the automatic clustering approach, in order to improve the accuracy of RSRP prediction. In fact, we adopt K-means clustering (KMC) to enhance the path loss exponent estimation. We use a dataset to test the proposed model using a set of comparative studies. The results showed that the proposed approach provides significant RSRP prediction capabilities for constructing REM, with a gain of about 3.3 dB in terms of root mean square error compared to the case without clustering.

Modern Diagnostic Imaging Classifications and Risk Factors for 6G-enabled Smart Health Systems

Modern Diagnostic Imaging Classifications and Risk Factors for 6G-enabled Smart Health Systems

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA's exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively.

Medibot for Emergency Vehicle

To create a medical robot that would be installed in an ambulance and use IoT to observe and communicate so that the patient might receive care before being brought to the hospital. In the case of a mishap, installing a finger print sensor will enable the hospital emergency room, police station, and the patient’s guardian to be informed of the unfamiliar patient’s bio-data. There are still significant problems with overpopulation and health-related illiteracy in India, and an accident-related mortality happens every minute. To build a clever smart health system, a MediBoT made up of sensors and microcontrollers is intended. It will assess the body’s condition and send information to the IoT.

Performance Comparison of ML Algorithms for Sustainable Smart Health Systems

Disease prognosis holds immense significance in healthcare due to its potential to greatly improve patient outcomes through early and precise diagnosis. Machine learning (ML) algorithms provide a robust avenue for disease prediction, employing patient data analysis to detect intricate patterns of specific ailments. Machine learning algorithms adeptly handle intricate and extensive datasets, uncovering latent patterns often eluding human observation. By considering diverse symptoms and their permutations, ML models yield precise forecasts concerning the probability of distinct diseases. The investigation begins by laying a basis in sustainable development concepts, recognising the need of resource optimisation, energy efficiency, and minimal environmental effect in the context of healthcare technology. Categorically, disease prediction methodologies fall under supervised and unsupervised learning categories, involving training algorithms on annotated datasets containing symptoms and corresponding diagnoses. These trained models can then anticipate diseases based on novel symptom profiles.