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%.

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.

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.

Cost analysis of radical resection of malignant breast tumors under the China Healthcare Security Diagnosis Related Groups payment system

BACKGROUND Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health.Therefore,this study examined patients who underwent breast cancer surgery,analyzed hospitalization costs and structure,and explored the impact of China Healthcare Security Diagnosis Related Groups(CHS-DRG)management on patient costs.It aimed to provide medical institutions with ways to reduce costs,optimize cost structures,reduce patient burden,and improve service efficiency.AIM To study the CHS-DRG payment system’s impact on breast cancer surgery costs.METHODS Using the CHS-DRG(version 1.1)grouping criteria,4073 patients,who underwent the radical resection of breast malignant tumors from January to December 2023,were included in the JA29 group;1028 patients were part of the CHS-DRG payment system,unlike the rest.Through an independent sample t-test,the length of hospital stay as well as total hospitalization,medicine and consumables,medical,nursing,medical technology,and management expenses were compared.Pearson’s correlation coefficient was used to test the cost correlation.RESULTS In terms of hospitalization expenses,patients in the CHS-DRG payment group had lower medical,nursing,and management expenses than those in the diagnosis-related group(DRG)non-payment group.For patients in the DRG payment group,the factors affecting the total hospitalization cost,in descending order of relevance,were medicine and consumable costs,consumable costs,medicine costs,medical costs,medical technology costs,management costs,nursing costs,and length of hospital stay.For patients in the DRG nonpayment group,the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses,consumable expenses,medical technology expenses,the cost of medicines,medical expenses,nursing expenses,length of hospital stay,and management expenses.CONCLUSION The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs,medical consumable costs,and the length of hospital stay while ensuring medical safety.

Violence study of healthcare workers and systems in the Caribbean:ViSHWaS-Caribbean study

BACKGROUND Violence against healthcare workers(HCWs)in the Caribbean continues to prevail yet remains underreported.Our aim is to determine the cause,traits,and consequences of violence on HCWs in the Caribbean.AIM To determine the cause,traits,and consequences of violence on HCWs in the Caribbean.METHODS This research adopted an online cross-sectional survey approach,spanning over eight weeks(between June 6th and August 9th,2022).The survey was generated using Research Electronic Data Capture forms and followed a snowballing strategy to contact individuals using emails,social media,text messages,etc.Logistic regression analysis was performed to evaluate the variables that influence violence,including gender,age,years of experience,institution type,and night shift frequency.RESULTS The survey was completed by 225 HCWs.Females comprised 61%.Over 51%of respondents belonged to the 21 to 35 age group.Dominica(n=61),Haiti(n=50),and Grenada(n=31)had the most responses.Most HCWs(49%)worked for government academic institutions,followed by community hospitals(23%).Medical students(32%),followed by attending physicians(22%),and others(16%)comprised the most common cadre of respondents.About 39%of the participants reported experiencing violence themselves,and 18%reported violence against colleague(s).Verbal violence(48%),emotional abuse(24%),and physical misconduct(14%)were the most common types of violence.Nearly 63%of respondents identified patients or their relatives as the most frequent aggressors.Univariate logistic regression analyses demonstrated that female gender(OR=2.08;95%CI:1.16-3.76,P=0.014)and higher frequency of night shifts(OR=2.22;95%CI:1.08-4.58,P=0.030)were associated with significantly higher odds of experiencing violence.More than 50%of HCWs felt less motivated and had decreased job satisfaction post-violent conduct.CONCLUSION A large proportion of HCWS in the Caribbean are exposed to violence,yet the phenomenon remains underreported.As a result,HCWs’job satisfaction has diminished.

Digital Disparities:How Artificial Intelligence Can Facilitate Anti-Black Racism in the U.S.Healthcare Sector

This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.

Early versus delayed necrosectomy in pancreatic necrosis:A population-based cohort study on readmission,healthcare utilization,and in-hospital mortality

BACKGROUND Acute necrotizing pancreatitis is a severe and life-threatening condition.It poses a considerable challenge for clinicians due to its complex nature and the high risk of complications.Several minimally invasive and open necrosectomy procedures have been developed.Despite advancements in treatment modalities,the optimal timing to perform necrosectomy lacks consensus.AIM To evaluate the impact of necrosectomy timing on patients with pancreatic necrosis in the United States.METHODS A national retrospective cohort study was conducted using the 2016-2019 Nationwide Readmissions Database.Patients with non-elective admissions for pancreatic necrosis were identified.The participants were divided into two groups based on the necrosectomy timing:The early group received intervention within 48 hours,whereas the delayed group underwent the procedure after 48 hours.The various intervention techniques included endoscopic,percutaneous,or surgical necrosectomy.The major outcomes of interest were 30-day readmission rates,healthcare utilization,and inpatient mortality.RESULTS A total of 1309 patients with pancreatic necrosis were included.After propensity score matching,349 cases treated with early necrosectomy were matched to 375 controls who received delayed intervention.The early cohort had a 30-day readmission rate of 8.6% compared to 4.8%in the delayed cohort(P=0.040).Early necrosectomy had lower rates of mechanical ventilation(2.9%vs 10.9%,P<0.001),septic shock(8%vs 19.5%,P<0.001),and in-hospital mortality(1.1%vs 4.3%,P=0.01).Patients in the early intervention group incurred lower healthcare costs,with median total charges of $52202 compared to$147418 in the delayed group.Participants in the early cohort also had a relatively shorter median length of stay(6 vs 16 days,P<0.001).The timing of necrosectomy did not significantly influence the risk of 30-day readmission,with a hazard ratio of 0.56(95%confidence interval:0.31-1.02,P=0.06).CONCLUSION Our findings show that early necrosectomy is associated with better clinical outcomes and lower healthcare costs.Delayed intervention does not significantly alter the risk of 30-day readmission.

Advancing Pediatric Hemodialysis Care through the Novel Intervention in Children’s Healthcare (NICH) Program

Racial, ethnic, and socioeconomic disparities present daunting hurdles that prevent equitable health outcomes for patients with end-stage kidney disease (ESKD) on hemodialysis. Additional resources, such as the Novel Intervention in Children’s Health (NICH) at Lucille Packard Children’s Hospital Stanford, provide individualized support to best assist families by assessing barriers to care with the goal of improving health outcomes. In this retrospective cohort study, we reviewed patients with ESKD on hemodialysis involved in NICH to explore if NICH serves as a liaison between the patients and multidisciplinary medical team and to explore if NICH helps patients better manage the challenges of end-stage kidney disease. Through the electronic medical record system, EPIC, we reviewed the patients’ surveys to identify barriers to care, which included school and life engagement difficulty, lack of mental health resources, food and transportation insecurity, and cultural/language barriers. We also tracked the number of hospitalizations and ED visits before and during the patients’ enrollment in NICH. We discovered that through NICH, the aforementioned barriers to care were eliminated, the number of hospitalizations and emergency department visits was reduced, and all patients transitioned from inactive to active on the transplant list. NICH successfully improved the health outcomes of these patients and empowered patients to be more engaged in their care.

Prevalence of Occupational Injury and Knowledge of Post-Exposure Prophylaxis Accessibility among Healthcare Workers in Mogadishu, Somalia

Introduction: Healthcare workers in Mogadishu, Somalia face significant occupational injury risks, particularly needle stick injuries, with 61.1% reporting incidents. This poses a serious threat to their health, leading to infections such as hepatitis B, hepatitis C, and HIV. Despite the high prevalence of injuries, awareness of Post-Exposure Prophylaxis (PEP) accessibility is relatively high, with 84.0% of respondents aware of it. However, there are gaps in knowledge and implementation, as evidenced by variations in availability of PEP. Improving workplace safety measures, providing comprehensive training on injury prevention and PEP protocols, and ensuring consistent availability of PEP in healthcare facilities are crucial steps to safeguard the well-being of healthcare workers in Mogadishu, Somalia. Methods: A cross-sectional study was conducted among hospital workers in Mogadishu, Somalia, focusing on professionals from various healthcare facilities. The study targeted nurses, doctors, laboratory personnel, and pharmacists. Purposive sampling was employed, resulting in a sample size of 383 calculated using Fisher’s sample size formula. Data were collected using coded questionnaires entered into Microsoft Excel 2019 and analyzed with SPSS software to generate frequencies and proportions, presented through frequency tables and pie figures. Results: The study in Mogadishu, Somalia, examined the prevalence of occupational injuries and knowledge of Post-Exposure Prophylaxis (PEP) accessibility among healthcare workers. Findings indicate a high prevalence of injuries, with 61.1% reporting incidents, predominantly needle stick injuries (60.6%). Despite the majority seeking prompt medical attention (72.0%), work-related illnesses affected 53.2% of respondents, notably work-related stress (59.5%). While most received training on injury and illness prevention (68.9%), gaps exist in PEP awareness, with 16.0% unaware of it. Nonetheless, 84.0% were aware, predominantly through health facilities (52.0%). Availability of PEP was reported by 71.3% in healthcare facilities, with variations in shift availability. The majority reported guidelines for PEP use (55.7%). Efforts are needed to bolster PEP awareness and ensure consistent availability in healthcare facilities to safeguard worker health. Conclusion: High prevalence of occupational injuries among healthcare workers, with needle stick injuries being the most common (60.6%). Despite this, 84.0% of respondents were aware of Post-Exposure Prophylaxis (PEP), primarily learning about it from health facilities (52.0%). While 71.3% reported the availability of PEP in their facility, 28.7% noted its unavailability. These results emphasize the need for improved education and accessibility of PEP to mitigate occupational injury risks.

Analysis of Hand Hygiene Knowledge,Compliance,and Influencing Factors Among Psychiatric Healthcare Workers

Objective:To evaluate the knowledge,compliance,and influencing factors of hand hygiene among psychiatric healthcare workers.Methods:68 healthcare workers who worked in the Department of Psychiatry between September 2023 and May 2024 were selected to assess their knowledge of hand hygiene and compliance by questionnaire as well as to analyze their influencing factors.Results:Knowledge of hand hygiene among healthcare workers was less than 90%,and doctors’knowledge was lower than that of nurses(P<0.05).The healthcare workers’compliance with hand hygiene was lower than 80%,and the adherence of doctors was lower than that of nurses(P<0.05).Analysis of influencing factors reveals that skepticism about the effectiveness of rapid disinfectants/hand washing,skin irritation from disinfectants/cleaning agents,and busy work schedules with time constraints are the main factors affecting healthcare workers’compliance with hand hygiene,with P<0.05 compared with the same group.Conclusion:Psychiatric healthcare workers’knowledge of hand hygiene as well as compliance with it is low,and there are various factors affecting it,so targeted training is required to strengthen their hand hygiene implementation.

Occupational health risks among healthcare workers during the COVID-19 pandemic in Saudi Arabia: preparing for the future pandemics

Background:This study aimed to assess the occupational risks encountered by Healthcare Workers(HCWs)in Saudi Arabia during the COVID-19 pandemic.Methods:A cross-sectional survey was carried out from May to October 2021.Using a multi-stage stratified random sampling technique,an online questionnaire was sent to the recruited HCWs,across Saudi Arabia.Results:Of the 768 HCWs recruited,702 participated in the survey.A significant majority(over 80%)reported working beyond 8 hours daily.COVID-19 infection,confirmed via PCR,was identified in 25%of the participants.Notably,infection was significantly correlated with direct or close contact(p=0.0007).Psychological distress was reported by 81%,with anxiety being the most prevalent(33%),followed by stress(19%),depression(17%),and insomnia(12%).Around 20%experienced headaches post-PPE use,while 14%reported adverse skin reactions,predominantly allergy and dermatitis.A concerning one-third of respondents reported exposure to violence.Conclusion:High infection rate,physical and psychological risks among HCWs especially those in direct contact with patients,reflect the need for enhancing the entirety of outbreak preparedness and response,specifically training.Active surveillance system,is crucial to adequately monitor and support HCWs during pandemic scenarios.

Predictive Healthcare: An IoT-Based ANFIS Framework for Diabetes Diagnosis

The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.

Analysis of the Application Effect of Structured Healthcare Education in Brittle Diabetic Patients

Objective:To explore the application effect of structured healthcare education in patients with brittle diabetes mellitus.Methods:188 brittle diabetic patients admitted to our hospital from May 2021 to December 2023 were selected as the study subjects,and were divided into the control group(n=94)and the observation group(n=94)according to the random number table method.The control group used conventional nursing intervention and the observation group used structured healthcare education.The general information,glycemic indexes,self-efficacy,compliance,and nursing satisfaction of patients in the two groups were observed.Results:There was no statistical significance in the basic information of the two groups of patients(P>0.05);after the intervention,the fasting plasma glucose,2-hour postprandial blood glucose,and HbA1c of the patients in the observation group were lower than those of the control group(P<0.001);after the intervention,the self-efficacy scores of the patients in the two groups increased,and the scores of the observation group were significantly higher than those of the control group(P<0.001);the total adherence rate of the patients in the observation group(90/95.75%)was significantly higher than that of the control group(80/90.10%)(χ^(2)=6.144,P<0.05);and the total satisfaction rate of patients in the observation group(92/97.87%)was significantly higher than that of the control group(78/82.98%)(χ^(2)=12.042,P<0.05).Conclusion:In patients with brittle diabetes mellitus,structured healthcare education can effectively control patients’blood glucose levels,improve patients’self-efficacy and adherence,and enhance patient satisfaction.

Accessibility and utilization of healthcare services among diabetic patients:Is diabetes a poor man’s ailment?

Diabetes is a non-communicable ailment that has adverse effects on the individual’s overall well-being and productivity in society.The main objective of this study was to examine the empirical literature concerning the association between diabetes and poverty and the accessibility and utilization of medical care services among diabetic patients.The diabetes literature was explored using a literature review approach.This review revealed that diabetes is an ailment that affects all individuals irrespective of socioeconomic status;however,its prevalence is high in low-income countries.Hence,despite the higher prevalence of diabetes in developing countries compared with developed countries,diabetes is not a poor man’s ailment because it affects individuals of all incomes.While the number of diabetic patients that access and utilize diabetes medical care services has increased over the years,some personal and institutional factors still limit patients’access to the use of diabetes care.Also,there is a lacuna in the diabetes literature concerning the extent of utilization of available healthcare services by diabetic patients.

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

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.

IOT Assisted Biomedical Monitoring Sensors for Healthcare in Human

The Internet of Things(IoT)is a concept that refers to the deployment of Internet Protocol(IP)address sensors in health care systems to monitor patients’health.It has the ability to access the Internet and collect data from sensors.Automated decisions are made after evaluating the information of illness people records.Patients’health and well-being can be monitored through IoT medical devices.It is possible to trace the origins of biological,medical equipment and processes.Human reliability is a major concern in user activity and fitness trackers in day-to-day activities.The fundamental challenge is to measure the efficiency of the human system accurately.Aim to maintain tabs on the well-being of humans;this paper recommends the use of wireless body area networks(WBANs)and artificial neural networks(ANN)to create an IoT-based healthcare framework for hospital information systems(IoT-HF-HIS).Our evaluation system uses a server to estimate how much computing power is needed for modeling,and simulations of the framework have been done using data rate and latency requirements are implementing the energy-aware technology presented in this paper.The proposed framework implements several hospital information system case studies by building a time-saving simulation environment.As the world’s population ages,more and more people suffer from physical and emotional ailments.Using the recommended strategy regularly has been proven user-friendly,reliable,and cost-effective,with an overall performance of 95.2%.

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial,while using healthcare data,to assess the advantages of data privacy against the possible drawbacks.Data from several sources must be combined for use in many data mining applications.The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures.Historically,numerous heuristics(e.g.,greedy search)and metaheuristics-based techniques(e.g.,evolutionary algorithm)have been created for the positive association rule in privacy preserving data mining(PPDM).When it comes to connecting seemingly unrelated diseases and drugs,negative association rules may be more informative than their positive counterparts.It is well-known that during negative association rules mining,a large number of uninteresting rules are formed,making this a difficult problem to tackle.In this research,we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’privacy.The applied approach dynamically determines the transactions to be interrupted for information hiding,as opposed to predefining them.This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining,one that is based on the Tabu-genetic optimization paradigm.Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets.Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions,as measured by the indicator of hiding failure.

Analysis of BrainMRI: AI-Assisted Healthcare Framework for the Smart Cities

The use of intelligent machines to work and react like humans is vital in emerging smart cities.Computer-aided analysis of complex and huge MRI(Mag-netic Resonance Imaging)scans is very important in healthcare applications.Among AI(Artificial Intelligence)driven healthcare applications,tumor detection is one of the contemporary researchfields that have become attractive to research-ers.There are several modalities of imaging performed on the brain for the pur-pose of tumor detection.This paper offers a deep learning approach for detecting brain tumors from MR(Magnetic Resonance)images based on changes in the division of the training and testing data and the structure of the CNN(Convolu-tional Neural Network)layers.The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy,including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states.The dataset is divided into test,and train subsets with a ratio of the training set to the validation set of 70:30.The main contribution of this paper is introdu-cing an optimum deep learning structure of CNN layers.The simulation results are obtained for 50 epochs in the training phase.The simulation results reveal that the optimum CNN architecture consists of four layers.