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%.

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

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

Differences between the healthcare systems of Quebec and France for the treatment of pain due to spinal disorders

In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contrast,private clinics provide expedited care but are relatively scarce and entail out-of-pocket expenses.Once a patient with pain caused by a spinal disorder meets a pain medicine specialist,spinal intervention is quickly performed when indicated,and patients are provided lifestyle advice.Transforaminal epidural steroid injections are frequently administered to patients with radicular pain,and steroid injections are administered on a facet joint to control low back or neck pain.Additionally,medial branch blocks are performed prior to thermocoagulation.France’s universal healthcare system ensures accessibility at controlled costs.It emphasizes physical activity and provides free physical therapy services.However,certain interventions,such as transforaminal and interlaminar epidural injections,are not routinely used in France owing to limited therapeutic efficacy and safety concerns.This underutilization may be a potential cause of chronic pain for many patients.By examining the differences,strengths,and weaknesses of these two systems,valuable insights can be gained for the enhancement of global spinal pain management strategies,ultimately leading to improved patient outcomes and satisfaction.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Managerial perspectives of scaling up robotic-assisted surgery in healthcare systems:A systematic literature review

Objectives Robotic-assisted surgery(RAS)is a minimally invasive technique practiced in multiple specialties.Standard training is essential for the acquisition of RAS skills.The cost of RAS is considered to be high,which makes it a burden for institutes and unaffordable for patients.This systematic literature review(SLR)focused on the various RAS training methods applied in different surgical specialties,as well as the cost elements of RAS,and was to summarize the opportunities and challenges associated with scaling up RAS.Methods An SLR was carried out based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses reporting guidelines.The PubMed,EBSCO,and Scopus databases were searched for reports from January 2018 through January 2024.Full-text reviews and research articles in the English language from Asia-Pacific countries were included.Articles that outlined training and costs associated with RAS were chosen.Results The most common training system is the da Vinci system.The simulation technique,which includes dry-lab,wet-lab,and virtual reality training,was found to be a common and important practice.The cost of RAS encompasses the installation and maintenance costs of the robotic system,the operation theatre rent,personnel cost,surgical instrument and material cost,and other miscellaneous charges.The synthesis of SLR revealed the challenges and opportunities regarding RAS training and cost.Conclusions The results of this SLR will help stakeholders such as decision-makers,influencers,and end users of RAS to understand the significance of training and cost in scaling up RAS from a managerial perspective.For any healthcare innovation to reach a vast population,cost-effectiveness and standard training are crucial.

Relation of COVID-19 with liver diseases and their impact on healthcare systems:The Portuguese case

BACKGROUND The impact caused by the coronavirus disease 2019(COVID-19)on the Portuguese population has been addressed in areas such as clinical manifestations,frequent comorbidities,and alterations in consumption habits.However,comorbidities like liver conditions and changes concerning the Portuguese population's access to healthcare-related services have received less attention.AIM To(1)Review the impact of COVID-19 on the healthcare system;(2)examine the relationship between liver diseases and COVID-19 in infected individuals;and(3)investigate the situation in the Portuguese population concerning these topics.METHODS For our purposes,we conducted a literature review using specific keywords.RESULTS COVID-19 is frequently associated with liver damage.However,liver injury in COVID-19 individuals is a multifactor-mediated effect.Therefore,it remains unclear whether changes in liver laboratory tests are associated with a worse prognosis in Portuguese individuals with COVID-19.CONCLUSION COVID-19 has impacted healthcare systems in Portugal and other countries;the combination of COVID-19 with liver injury is common.Previous liver damage may represent a risk factor that worsens the prognosis in individuals with COVID-19.

Intestinal complications in patients with Crohn's disease in the Brazilian public healthcare system between 2011 and 2020

BACKGROUND This is a secondary database study using the Brazilian public healthcare system database.AIM To describe intestinal complications(ICs)of patients in the Brazilian public healthcare system with Crohn’s disease(CD)who initiated and either only received conventional therapy(CVT)or also initiated anti-tumor necrosis factor(anti-TNF)therapy between 2011 and 2020.METHODS This study included patients with CD[international classification of diseases–10th revision(ICD-10):K50.0,K50.1,or K50.8](age:≥18 years)with at least one claim of CVT(sulfasalazine,azathioprine,mesalazine,or methotrexate).IC was defined as a CD-related hospitalization,pre-defined procedure codes(from rectum or intestinal surgery groups),and/or associated disease(pre-defined ICD-10 codes),and overall(one or more type of ICs).RESULTS In the 16809 patients with CD that met the inclusion criteria,the mean follow-up duration was 4.44(2.37)years.In total,14697 claims of ICs were found from 4633 patients.Over the 1-and 5-year of follow-up,8.3%and 8.2%of the patients with CD,respectively,presented at least one IC,of which fistula(31%)and fistulotomy(48%)were the most commonly reported.The overall incidence rate(95%CI)of ICs was 6.8(6.5–7.04)per 100 patient years for patients using only-CVT,and 9.2(8.8–9.6)for patients with evidence of anti-TNF therapy.CONCLUSION The outcomes highlighted an important and constant rate of ICs over time in all the CD populations assessed,especially in patients exposed to anti-TNF therapy.This outcome revealed insights into the real-world treatment and complications relevant to patients with CD and highlights that this disease remains a concern that may require additional treatment strategies in the Brazilian public healthcare system.

Secured Cyber Security Algorithm for Healthcare System Using Blockchain Technology

Blockchain technology is critical in cyber security.The most recent cryptographic strategies may be hacked as efforts are made to build massive elec-tronic circuits.Because of the ethical and legal implications of a patient’s medical data,cyber security is a critical and challenging problem in healthcare.The image secrecy is highly vulnerable to various types of attacks.As a result,designing a cyber security model for healthcare applications necessitates extra caution in terms of data protection.To resolve this issue,this paper proposes a Lionized Golden Eagle based Homomorphic Elapid Security(LGE-HES)algorithm for the cybersecurity of blockchain in healthcare networks.The blockchain algorithm preserves the security of the medical image by performing hash function.The execution of this research is carried out by MATLAB software.The suggested fra-mework was tested utilizing Computed Tumor(CT)pictures and MRI image data-sets,and the simulation results revealed the proposed model’s profound implications.During the simulation,94.9%of malicious communications were recognized and identified effectively,according to the total outcomes statistics.The suggested model’s performance is also compared to that of standard approaches in terms of Root Mean Square Error(RMSE),Peak Signal to Noise Ratio(PSNR),Mean Square Error(MSE),time complexity,and other factors.

Medi-Block Record Secure Data Sharing in Healthcare System:Issues,Solutions and Challenges

With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

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.

Telepresence Robots and Controlling Techniques in Healthcare System

In this era of post-COVID-19,humans are psychologically restricted to interact less with other humans.According to the world health organization(WHO),there are many scenarios where human interactions cause severe multiplication of viruses from human to human and spread worldwide.Most healthcare systems shifted to isolation during the pandemic and a very restricted work environment.Investigations were done to overcome the remedy,and the researcher developed different techniques and recommended solutions.Telepresence robot was the solution achieved by all industries to continue their operations but with almost zero physical interaction with other humans.It played a vital role in this perspective to help humans to perform daily routine tasks.Healthcare workers can use telepresence robots to interact with patients who visit the healthcare center for initial diagnosis for better healthcare system performance without direct interaction.The presented paper aims to compare different telepresence robots and their different controlling techniques to perform the needful in the respective scenario of healthcare environments.This paper comprehensively analyzes and reviews the applications of presented techniques to control different telepresence robots.However,our feature-wise analysis also points to specific technical,appropriate,and ethical challenges that remain to be solved.The proposed investigation summarizes the need for further multifaceted research on the design and impact of a telepresence robot for healthcare centers,building on new perceptions during the COVID-19 pandemic.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life.The healthcare industry is developing very rapidly with the help of advanced technologies.Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises,as well as by patients from their mobile devices through communication interfaces.These systems promote reliable and remote interactions between patients and healthcare professionals.However,there are several limitations to these innovative cloud computing-based systems,namely network availability,latency,battery life and resource availability.We propose a hybrid mobile cloud computing(HMCC)architecture to address these challenges.Furthermore,we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture.We compare them,to identify the strengths and weaknesses of each algorithm;and provide their comparative results,to show latency and energy consumption performance.Challenging issues for cloudbased healthcare systems are discussed in detail.

DeepGan-Privacy Preserving of HealthCare System Using DL

The challenge of encrypting sensitive information of a medical image in a healthcare system is still one that requires a high level of computing complexity,despite the ongoing development of cryptography.After looking through the previous research,it has become clear that the security issues still need to be looked into further because there is room for expansion in the research field.Recently,neural networks have emerged as a cost-effective and effective optimization strategy in terms of providing security for images.This revelation came about as a result of current developments.Nevertheless,such an implementation is a technique that is expensive to compute and does not handle the huge variety of different assaults that may be made on pictures.The primary objective of the system that has been described is to provide evidence of a complex framework in which deep neural networks have been applied to improve the efficiency of basic encryption techniques.Our research has led to the development and proposal of an enhanced version of methods that have previously been used to encrypt pictures.Instead,the generative adversarial network(GAN),commonly known as GAN,will serve as the learning network that generates the private key.The transformation domain,which reflects the one-of-a-kind fashion of the private key that is to be formed,is also meant to lead the learning network in the process of actually accomplishing the private key creation procedure.This scheme may be utilized to train an excellent Deep Neural Networks(DNN)model while instantaneously maintaining the confidentiality of training medical images.It was tested by the proposed approach DeepGAN on open-source medical datasets,and three sets of data:The Ultrasonic Brachial Plexus,the Montgomery County Chest X-ray,and the BraTS18.The findings indicate that it is successful in maintaining both performance and privacy,and the findings of the assessment and the findings of the security investigation suggest that the development of suitable generation technologies is capable of generating private keys with a high level of security.

Estimating the Monetary Value of Hours Lost to the Nigerian Public Healthcare System When Full-Time Government Employee Doctors Engage in Dual Practice

Background: Dual Practice (DP) allows full-time public sector doctors to concurrently offer the same clinical services in the private sector. The debate against this practice seems to be largely influenced by its potential to reduce the contracted hours in the public sector and shift attention to private work. Purpose: The purpose of this secondary research is to estimate the monetary value of hours lost to the Nigerian public healthcare system when full-time government employee doctors are engaged in private practice. It attempts to quantify the amount of resource outflow from the public system due to absences and lateness arising from competition for time between the public system’s contracted hours and private practice. Methods: Sensitivity analysis in Excel 2010 was used to calculate doctors’ hourly pay in the public sector using the 2015 Consolidated Medical Salary Structure for medical and dental officers in Nigeria’s federal public service. The parameters used for the calculation were the official 40-hour working week and the average monthly gross pay of doctors on different grade levels. Hypothetical scenarios of hours lost due to absences associated with DP were created. The value of different hypothetical hour losses by the percentage of doctors assumed to engage in dual practice across all doctor grade levels was then computed. Results: The estimated annual value of hours lost from dual practice to a single public tertiary care hospital was N4,851,754 or 15,855 USD (best case scenario) and N19,407,017 or 63,422 USD (worst case scenario) for the normal routine work and N1,800,133 or 5883 USD (best case scenario) and N3,600,266 or 11,766 USD (worst case scenario) for the on-call duty. Conclusion: The government may have been paying salaries for large volumes of work not rendered in the public sector. The overall financial impact of dual practice in the Nigerian public system might be negative.

Studies on Multi-Criteria Decision-Making-Based Healthcare Systems Using the Machine Learning

There is a lot of information in healthcare and medical records.However,it is challenging for humans to turn data into information and spot hidden patterns in today’s digitally based culture.Effective decision support technologies can help medical professionals find critical information concealed in voluminous data and support their clinical judgments and in different healthcare management activities.This paper presented an extensive literature survey for healthcare systems using machine learning based on multi-criteria decision-making.Various existing studies are considered for review,and a critical analysis is being done through the reviews study,which can help the researchers to explore other research areas to cater for the need of the field.

Intelligent Disease Diagnosis Model for Energy Aware Cluster Based IoT Healthcare Systems

In recent days,advancements in the Internet of Things(IoT)and cloud computing(CC)technologies have emerged in different application areas,particularly healthcare.The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server.Therefore,energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices.At the same time,the advent of deep learning(DL)models helps to analyze the healthcare data in the cloud server for decision making.With this motivation,this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems,called IDDM-EAC technique.The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition,clustering,and disease diagnosis.In addition,the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering(CSOEAC)technique to group the IoT devices into clusters and select cluster heads(CHs).Moreover,a new coyote optimization algorithm(COA)with deep belief network(DBN),called COA-DBN technique is employed for the disease diagnostic process.The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model.In order to inspect the betterment of the IDDM-EAC technique,a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository.The experimental results demonstrate the promising performance with the minimal total energy consumption of 63%whereas the EEPSOC,ABC,GWO,and ACO algorithms have showcased a higher total energy consumption of 69%,78%,83%,and 84%correspondingly.

IoMT-Enabled Fusion-Based Model to Predict Posture for Smart Healthcare Systems

Smart healthcare applications depend on data from wearable sensors(WSs)mounted on a patient’s body for frequent monitoring information.Healthcare systems depend on multi-level data for detecting illnesses and consequently delivering correct diagnostic measures.The collection of WS data and integration of that data for diagnostic purposes is a difficult task.This paper proposes an Errorless Data Fusion(EDF)approach to increase posture recognition accuracy.The research is based on a case study in a health organization.With the rise in smart healthcare systems,WS data fusion necessitates careful attention to provide sensitive analysis of the recognized illness.As a result,it is dependent on WS inputs and performs group analysis at a similar rate to improve diagnostic efficiency.Sensor breakdowns,the constant time factor,aggregation,and analysis results all cause errors,resulting in rejected or incorrect suggestions.This paper resolves this problem by using EDF,which is related to patient situational discovery through healthcare surveillance systems.Features of WS data are examined extensively using active and iterative learning to identify errors in specific postures.This technology improves position detection accuracy,analysis duration,and error rate,regardless of user movements.Wearable devices play a critical role in the management and treatment of patients.They can ensure that patients are provided with a unique treatment for their medical needs.This paper discusses the EDF technique for optimizing posture identification accuracy through multi-feature analysis.At first,the patients’walking patterns are tracked at various time intervals.The characteristics are then evaluated in relation to the stored data using a random forest classifier.

A Simulation Engine for Stochastic Timed Petri Nets and Application to Emergency Healthcare Systems

In many service delivery systems,the quantity of available resources is often a decisive factor of service quality.Resources can be personnel,offices,devices,supplies,and so on,depending on the nature of the services a system provides.Although service computing has been an active research topic for decades,general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen.Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century.Stochastic timed Petri nets(STPN),an extension to regular Petri nets,are a powerful tool for system performance evaluation.However,we did not find any single existing STPN software tool that supports all timed transition firing policies and server types,not to mention resource provisioning and requirement analysis.This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs.resource provisioning.The power of the simulation system is illustrated by an application to emergency health care systems.

The Development of the Qatar Healthcare System: A Review of the Literature

Background: Qatar, one of the smallest and wealthiest countries in the world, is a newly emerging healthcare system. Medical leadership in Qatar has had to create an infrastructure for medical care over the past twenty years. The purpose of this paper is to review the challenges and achievements of the newly emerging Qatar healthcare system. Methods: PubMed was searched using MESH terms: Qatar, healthcare, medical development, medical insurance and medical history. Websites of the World Bank, CIA fact book, Qatar Ministry of Health, Hamad Medical Corporation, Organization for Economic Co-operation and Development and the US State department were searched for information about Qatar’s healthcare system and its history. Results: Qatar is a rapidly growing, multicultural country with over 80 nationalities represented. Qatar has developed a healthcare system with universal coverage. Up until 2014, the government has subsidized all care. There are plans to develop a medical insurance system. Conclusions: Qatar has experienced the rapid development of a healthcare system over the past twenty years. The government has centrally controlled growth and development. An examination of the unique challenges to building a Qatari healthcare system will be useful in considering how to develop medical infrastructure in other countries.