Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Teamwork Optimization with Deep Learning Based Fall Detection for IoT-Enabled Smart Healthcare System

The current advancement in cloud computing,Artificial Intelligence(AI),and the Internet of Things(IoT)transformed the traditional healthcare system into smart healthcare.Healthcare services could be enhanced by incorporating key techniques like AI and IoT.The convergence of AI and IoT provides distinct opportunities in the medical field.Fall is regarded as a primary cause of death or post-traumatic complication for the ageing population.Therefore,earlier detection of older person falls in smart homes is required to improve the survival rate of an individual or provide the necessary support.Lately,the emergence of IoT,AI,smartphones,wearables,and so on making it possible to design fall detection(FD)systems for smart home care.This article introduces a new Teamwork Optimization with Deep Learning based Fall Detection for IoT Enabled Smart Healthcare Systems(TWODLFDSHS).The TWODL-FDSHS technique’s goal is to detect fall events for a smart healthcare system.Initially,the presented TWODL-FDSHS technique exploits IoT devices for the data collection process.Next,the TWODLFDSHS technique applies the TWO with Capsule Network(CapsNet)model for feature extraction.At last,a deep random vector functional link network(DRVFLN)with an Adam optimizer is exploited for fall event detection.A wide range of simulations took place to exhibit the enhanced performance of the presentedTWODL-FDSHS technique.The experimental outcomes stated the enhancements of the TWODL-FDSHS method over other models with increased accuracy of 98.30%on the URFD dataset.

Heap Based Optimization with Deep Quantum Neural Network Based Decision Making on Smart Healthcare Applications

The concept of smart healthcare has seen a gradual increase with the expansion of information technology.Smart healthcare will use a new generation of information technologies,like artificial intelligence,the Internet of Things(IoT),cloud computing,and big data,to transformthe conventional medical system in an all-around way,making healthcare highly effective,more personalized,and more convenient.This work designs a new Heap Based Optimization with Deep Quantum Neural Network(HBO-DQNN)model for decision-making in smart healthcare applications.The presented HBO-DQNN modelmajorly focuses on identifying and classifying healthcare data.In the presented HBO-DQNN model,three stages of operations were performed.Data normalization is applied to pre-process the input data at the initial stage.Next,the HBO algorithm is used in the second stage to choose an optimal set of features from the healthcare data.At last,the DQNN model is exploited for healthcare data classification.A series of experiments were carried out to portray the promising classifier results of the HBO-DQNN model.The extensive comparative study reported the improvements of the HBO-DQNN method over other existing models with maximum accuracy of 97.05%and 95.72%under the colon cancer and lymphoma dataset.

Telemedicine and Smart Healthcare—The Role of Artificial Intelligence, 5G, Cloud Services, and Other Enabling Technologies

This paper discusses telemedicine and the employment of advanced mobile technologies in smart healthcare delivery. It covers the technological advances in connected smart healthcare, including the roles of artificial intelligence, machine learning, 5G and IoT platforms, and other enabling technologies. It also presents the challenges and potential risks that could arise from delivering connected smart healthcare services. Healthcare delivery is witnessing revolutions engineered by the developments in mobile connectivity and the plethora of platforms, applications, sensors, devices, and equipment that go along with it. Human society is evolving fast in response to these technological developments, which are also pushing the connectivity-providing sector to create and adopt new waves of network technologies. Consequently, new communications technologies have been introduced into the healthcare system and many novel applications have been developed to make it easier for sharing data in various forms and volumes within health-related services. These applications have also made it possible for telemedicine to be effectively adopted. This paper provides an overview of some of the recent developments within the space of mobile connectivity and telemedicine.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare

Chronic diseases are a growing concern worldwide,with nearly 25% of adults suffering from one or more chronic health conditions,thus placing a heavy burden on individuals,families,and healthcare systems.With the advent of the“Smart Healthcare”era,a series of cutting-edge technologies has brought new experiences to the management of chronic diseases.Among them,smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state.However,how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management,in terms of quality of life,patient outcomes,and privacy protection,is an urgent issue that needs to be addressed.Artificial intelligence(AI)can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases.In addition,blockchain can improve healthcare services by authorizing decentralized data sharing,protecting the privacy of users,providing data empowerment,and ensuring the reliability of data management.Integrating AI,blockchain,and wearable technology could optimize the existing chronic disease management models,with a shift from a hospital-centered model to a patient-centered one.In this paper,we conceptually demonstrate a patient-centric technical framework based on AI,blockchain,and wearable technology and further explore the application of these integrated technologies in chronic disease management.Finally,the shortcomings of this new paradigm and future research directions are also discussed.

Convergence Platform of Cloud Computing and Internet of Things (IoT) for Smart Healthcare Application

Internet of Things (IoT) is a widely distributed network which requires small amount of power supply having limited storage and processing capacity. On the other hand, Cloud computing has virtually unlimited storage and processing capabilities and is a much more mature technology. Therefore, combination of Cloud computing and IoT can provide the best performance for users. Cloud computing nowadays provides lifesaving healthcare application by collecting data from bedside devices, viewing patient information and diagnose in real time. There may some concerns about security and other issues of the patient’s data but utilization of IoT and Cloud technologies in healthcare industry would open a new era in the field of healthcare. To ensure basic healthcare needs of the people in the rural areas, we have proposed Cloud-IoT based smart healthcare system. In this system various types of sensors (Temperature, Heart bit, ECG, etc.) are equipped in the patient side to sense the patient’s physiological data. For securing data RSA based authentication algorithm and mitigation of several security threats have been used. The sensed data will process and store in the Cloud server. Stored data can be used by the authorized and/or concerned medical practitioner upon approved by the user for patient caring.

Smart Healthcare Using Data-Driven Prediction of Immunization Defaulters in Expanded Program on Immunization (EPI)

Immunization is a noteworthy and proven tool for eliminating lifethreating infectious diseases,child mortality and morbidity.Expanded Program on Immunization(EPI)is a nation-wide program in Pakistan to implement immunization activities,however the coverage is quite low despite the accessibility of free vaccination.This study proposes a defaulter prediction model for accurate identification of defaulters.Our proposed framework classifies defaulters at five different stages:defaulter,partially high,partially medium,partially low,and unvaccinated to reinforce targeted interventions by accurately predicting children at high risk of defaulting from the immunization schedule.Different machine learning algorithms are applied on Pakistan Demographic and Health Survey(2017–18)dataset.Multilayer Perceptron yielded 98.5%accuracy for correctly identifying children who are likely to default from immunization series at different risk stages of being defaulter.In this paper,the proposed defaulters’prediction framework is a step forward towards a data-driven approach and provides a set of machine learning techniques to take advantage of predictive analytics.Hence,predictive analytics can reinforce immunization programs by expediting targeted action to reduce dropouts.Specially,the accurate predictions support targeted messages sent to at-risk parents’and caretakers’consumer devices(e.g.,smartphones)to maximize healthcare outcomes.

Correlation between psychological traits and the use of smart medical services in young and middle-aged adults: An observational study

BACKGROUND Psychological problems affect economic development.However,there is a huge gap between mental health service resources and mental health service needs.Existing mental health service technology and platforms cannot meet all the diverse mental health needs of people.Smart medicine is a new medical system based online that can effectively improve the quality and efficiency of medical services and make mental health services accessible.AIM To explore the level of intelligent medical use among young and middle-aged people and its correlation with psychological factors.METHODS Convenience sampling was used to select 200 young and middle-aged patients with medical experience at the Third People's Hospital of Chengdu between January 2022 and January 2023 as the research subjects.The general condition Questionnaire,Eysenck Personality Questionnaire,Symptom Checklist-90,General Health Questionnaire,and Smart Medical Service Use Intention Questionnaire were used to collect data.Pearson’s correlation was used to analyze the correlation between the participants’willingness to use smart medical services and their personality characteristics,psychological symptoms,and mental health.RESULTS The results revealed that the mental health of young and middle-aged people was poor,and some had psycho-logical problems such as anxiety,depression,and physical discomfort.Familiarity,acceptance,and usage of smart healthcare in this population are at a medium level,and these levels correlate with psychological characteristics.Acceptance was positively correlated with E,and negatively correlated with P,anxiety,fear,anxiety/insomnia,and social dysfunction.The degree of use was negatively correlated with P,obsessive-compulsive symptoms,depression,anxiety,hostility,paranoia,and somatic symptoms.CONCLUSION The familiarity,acceptance,and usage of smart medical services among the middle-aged and young groups are related to various psychological characteristics.

BIoMT:A Blockchain-Enabled Healthcare Architecture for Information Security in the Internet of Medical Things

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things(IoMT).The existing cloud-based,centralized IoMT architectures are vulnerable to multiple security and privacy problems.The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks.This article presents a private and easily expandable blockchain-based framework for the IoMT.The proposed framework contains several participants,including private blockchain,hospitalmanagement systems,cloud service providers,doctors,and patients.Data security is ensured by incorporating an attributebased encryption scheme.Furthermore,an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network.The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner.The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency.Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations.The average latency is 61 ms and 16 ms for read and write operations,respectively.

IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective and smarter healthcare facilities.IoMT has the potential to shape the future of clinical research in the healthcare sector.Wearable sensors,patients,healthcare providers,and caregivers can connect through an IoMT network using software,information,and communication technology.Ambient assisted living(AAL)allows the incorporation of emerging innovations into the routine life events of patients.Machine learning(ML)teaches machines to learn from human experiences and to use computer algorithms to“learn”information directly instead of relying on a model.As the sample size accessible for learning increases,the performance of the algorithms improves.This paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network(CNN)algorithm for fast analysis,improved decision-making,and enhanced treatment support.The simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

Wireless Body Area Networks for Pervasive Healthcare and Smart Environments

Wireless body area networks （WBANs） use RF communication for interconnection of tiny sensor nodes located in, on, or in close prox- imity to the human body. A WBAN enables physiological signals, physical activity, and body position to be continuously monitored.

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

A critical literature review of security and privacy in smart home healthcare schemes adopting IoT&blockchain:Problems,challenges and solutions

Protecting private data in smart homes,a popular Internet-of-Things(IoT)application,remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks.Recently,smart healthcare has leveraged smart home systems,thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner.However,proof-of-authority(PoA)-based blockchain distributed ledger technology(DLT)has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes.This review elicits some concerns,issues,and problems that have hindered the adoption of blockchain and IoT(BCoT)in some domains and suggests requisite solutions using the aging-in-place scenario.Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains.The study discusses recent findings,opportunities,and barriers,and provides recommendations that could facilitate the continuous growth of blockchain applications in healthcare.Lastly,the study explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process,including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing,as well as ethical trust in personal information disclosure,as a solution direction.The proposed authorisation framework could guarantee data ownership,conditional access management,scalable and tamper-proof data storage,and a more resilient system against threat models such as interception and insider attacks.

A Secure and Efficient Signature Scheme for IoT in Healthcare

To provide faster access to the treatment of patients,healthcare system can be integrated with Internet of Things to provide prior and timely health services to the patient.There is a huge limitation in the sensing layer as the IoT devices here have low computational power,limited storage and less battery life.So,this huge amount of data needs to be stored on the cloud.The information and the data sensed by these devices is made accessible on the internet from where medical staff,doctors,relatives and family members can access this information.This helps in improving the treatment as well as getting faster medical assistance,tracking of routine activities and health focus of elderly people on frequent basis.However,the data transmission from IoT devices to the cloud faces many security challenges and is vulnerable to different security and privacy threats during the transmission path.The purpose of this research is to design a Certificateless Secured Signature Scheme that will provide a magnificent amount of security during the transmission of data.Certificateless signature,that removes the intricate certificate management and key escrow problem,is one of the practical methods to provide data integrity and identity authentication for the IoT.Experimental result shows that the proposed scheme performs better than the existing certificateless signature schemes in terms of computational cost,encryption and decryption time.This scheme is the best combination of high security and cost efficiency and is further suitable for the resource constrained IoT environment.

A Usability Management Framework for Securing Healthcare Information System

Transformation from conventional business management systems tosmart digital systems is a recurrent trend in the current era. This has led to digitalrevolution, and in this context, the hardwired technologies in the software industry play a significant role However, from the beginning, software security remainsa serious issue for all levels of stakeholders. Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data, compromising the organizations’ reputation that translates into, financial losses andcompromising software usability as well. Most of the data breaches are financiallymotivated, especially in the healthcare sector. The cyber invaders continuouslypenetrate the E- Health data because of the high cost of the data on the darkweb. Therefore, security assessment of healthcare web-based applicationsdemands immediate intervention mechanisms to weed out the threats of cyberattacks for the sake of software usability. The proposed disclosure is a unique process of three phases that are combined by researchers in order to produce andmanage usability management framework for healthcare information system. Inthis most threatened time of digital era where, Healthcare data industry has bornethe brunt of the highest number of data breach episodes in the last few years. Thekey reason for this is attributed to the sensitivity of healthcare data and the highcosts entailed in trading the data over the dark web. Hence, usability managementof healthcare information systems is the need of hour as to identify the vulnerabilities and provide preventive measures as a shield against the breaches. The proposed unique developed model of usability management workflow is preparedby associating steps like learn;analyze and manage. All these steps gives an allin one package for the healthcare information management industry because thereis no systematic model available which associate identification to implementationsteps with different evaluation steps.

M-IDM: A Multi-Classication Based Intrusion Detection Model in Healthcare IoT

In recent years,the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected.Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset,such as the NSL-KDD dataset.However,such approaches do not reect the features that exist in real medical scenarios,leading to failure in potential threat detection.To address this problem,we proposed a novel intrusion classication architecture known as a Multi-class Classication based Intrusion Detection Model(M-IDM),which typically relies on data collected by real devices and the use of convolutional neural networks(i.e.,it exhibits better performance compared with conventional machine learning algorithms,such as naïve Bayes,support vector machine(SVM)).Unlike existing studies,the proposed architecture employs the actual healthcare IoT environment of National Cancer Center in South Korea and actual network data from real medical devices,such as a patient’s monitors(i.e.,electrocardiogram and thermometers).The proposed architecture classies the data into multiple classes:Critical,informal,major,and minor,for intrusion detection.Further,we experimentally evaluated and compared its performance with those of other conventional machine learning algorithms,including naïve Bayes,SVM,and logistic regression,using neural networks.

桂林中老年群体智慧医疗服务使用意愿调查:基于技术采纳与应用整合理论模型

目的对中老年群体智慧医疗服务使用情况及其影响因素进行调查分析,为医疗机构优化智慧医疗服务、推动中老年群体数字鸿沟治理提供参考。方法在广西壮族自治区桂林市采用线下问卷调查、访谈等方法收集数据,结合技术采纳与应用整合理论模型,使用SPSS 26.0和AMOS 24.0进行不同变量描述统计、单因素分析、不同维度相关分析和结构方程模型路径分析,研究中老年群体在使用智慧医疗服务过程中的影响因素。结果桂林市中老年群体对智慧医疗服务的使用意愿得分为(3.14±0.76)分,除了性别,不同变量对中老年群体的使用意愿差异分别有统计学意义(P<0.05)。中老年群体的绩效期望、社群影响、配合情况对中老年群体使用智慧医疗服务意愿有正向影响,努力期望对于使用意愿的影响不明显。结论桂林市中老年群体智慧医疗服务使用意愿还有很大的提升空间,要从各维度入手提高中老年群体的使用意愿,多方共同努力推进智慧医疗服务的使用。

Discrete GWO Optimized Data Aggregation for Reducing Transmission Rate in IoT

The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies.Early diagnosis of many diseases will improve the patient life.The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation methods.The data generated through these technologies will demand the bandwidth,data rate,latency of the network.In this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the patient.The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO.Aggregation scheme will reduce the number of transmissions over the network.The aggregated data are preprocessed at edge node to remove the noise for better diagnosis.Edge node will reduce the overhead of cloud server.The aggregated data are forward to cloud server for central storage and diagnosis.This proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the system.Energy cost for proposed system for 300 nodes is 0.34μJ.Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ respectively.The optimization approaches and encryption method will ensure the data privacy.

A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the country.Transforming healthcare management into Smart hospital systems is one of them.Secure hospital management systems which are breach-proof only can be termed as effective smart hospital systems.Given the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart ideas.In this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical practices.Further,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital system.The framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare data.Moreover,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare sector.Internet of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare systems.These entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor devices.The advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making process.Skin cancer is the deadliest disease which affects people across the globe.Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions.The current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare environments.Primarily,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further diagnosis.Besides,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image segmentation.Followed by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature extractor.In addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic images.An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset.The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.