E-health Systems Research Articles

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events.

EmbedR-Net: Using CNN to Embed Mark With Recovery Through Deep Convolutional GAN for Secure eHealth Systems

The goal of security is to protect digital assets, devices and services from being disrupted, exploited or stolen by unauthorised users. It is also about having reliable media information available at the right time. However, the media distortion will pose many potential risks in eHealth systems. In this paper, a new data hiding method called EmbedR-Net based on Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) is proposed, which can prevent the copyright violation of the medical images. First, a CNN based embedder network is designed for imperceptibly hiding medical images as marks in the carrier image. Second, we compute the diagonal value of the marked image for hidden mark recovery. Last, the DCGAN network is designed to robustly recover the hidden mark using the diagonal value of the marked image. Compared to existing methods, experiments on five different datasets have shown that the proposed EmbedR-Net obtains superior performance.

HealthFort: A Cloud-Based eHealth System With Conditional Forward Transparency and Secure Provenance via Blockchain

HealthFort: A Cloud-Based eHealth System With Conditional Forward Transparency and Secure Provenance via Blockchain

LAEBLE: A Lightweight Authentication and Encryption Mechanism for the E-Health System in Bluetooth Low Energy

LAEBLE: A Lightweight Authentication and Encryption Mechanism for the E-Health System in Bluetooth Low Energy

Co-design and Consultation Ensure Consumer Needs Are Met: Building an eHealth Platform for Children with Inflammatory Bowel Disease

BackgroundCrohn’s Colitis Care is an adult inflammatory bowel disease eHealth system. Crohn’s Colitis Care required additional pediatric functionality to enable life-long records and mitigate transition inadequacies.AimThis study describes and evaluates a consensus method developed to ensure consumer needs were met.MethodsPediatric-specific functionality and associated resources considered important for inclusion were developed by a clinician consensus group. This group was divided into thematic subgroups and underwent two voting rounds. The content validity index was used to determine items reaching consensus. Children with inflammatory bowel disease and their parents were later shown a descriptive list of non-clinical inclusion topics proposed by the consensus group, and asked to vote on whether topic-related functionality and resources should be included.ResultsThe consensus process consulted 189 people in total (38 clinicians, 32 children with inflammatory bowel disease and 119 parents). There was agreement across all groups to incorporate functionality and resources pertaining to quality of life, mental health, self-management, and transition readiness; however, divergence was seen for general inflammatory bowel disease facts, your inflammatory bowel disease history, and satisfaction. Cost saw the greatest disparity, being less supported by consumers compared to clinicians. Over 75% of consumers agreed it would be okay for appointments to take longer for survey completion, and > 90% thought Crohn’s Colitis Care should allow consumers to ask their treating team questions.ConclusionsWidespread consumer co-design and consultation were important in unveiling differing perspectives to ensure Crohn’s Colitis Care was built to support both consumer and clinician perspectives.Graphical Consumers collaborate to create a list of functionality and resources to be included in software (left), influencing the final product build (right).

Assessing the Knowledge, Practice, and Attitudes of Healthcare Workers on E-Health Systems in Oshakati District, Namibia

The study aimed to assess healthcare workers’ knowledge, practice, and attitudes on e-Health systems in Oshakati district. There were 56 healthcare workers from different health facilities (Ekamba clinic, Ongwediva Health Center, Eluwa clinic, Oshakati state hospital - PHC) Oshakati district. The educational backgrounds of many healthcare workers regarding electronic health are little in many developing countries. Many healthcare professionals in the health industry find it difficult to use and it takes time for them to adapt to the e-health systems and thus contributing to ignorance behaviors, which leads to digitized data discrepancy. e-Health in developing countries are challenging, as there are no sufficient funds to keep such developments (e-Health systems) running and active all the time. Conclusively Limited power and internet connectivity is accessible in developing countries’ rural areas. The aim of the study was to assess the knowledge, practice, and attitudes of healthcare workers on e-Health systems. The study design for this study was a cross-sectional study design that falls under descriptive studies. The sample size consisted of 56 respondents from 5 different health facilities in Oshakati district. A simple random sampling method was used, and quantitative method which is a self-administered questionnaire was used as a data collection tool.

EHealth literacy among hospital health care providers: a systematic review

BackgroundeHealth literacy is a key concept in the implementation of eHealth resources. However, most eHealth literacy definitions and frameworks are designed from the perceptive of the individual receiving eHealth care, which do not include health care providers’ eHealth literacy or acceptance of delivering eHealth resources.AimsTo identify existing research on eHealth literacy domains and measurements and identify eHealth literacy scores and associated factors among hospital health care providers.MethodsThis systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist. A systematic literature search was conducted in MEDLINE, Cinahl, Embase, Scopus, PEDro, AMED and Web of Science. Quantitative studies assessing eHealth literacy with original research, targeting hospital health care providers were included. Three eHealth literacy domains based on the eHealth literacy framework were defined a priori; (1) Individual eHealth literacy, (2) Interaction with the eHealth system, and (3) Access to the system. Pairs of authors independently assessed eligibility, appraised methodological quality and extracted data.ResultsFourteen publications, of which twelve publications were conducted in non-Western countries, were included. In total, 3,666 health care providers within eleven different professions were included, with nurses being the largest group. Nine of the included studies used the eHealth literacy scale (eHEALS) to measure eHealth literacy, representing the domain of individual eHealth literacy. A minority of the studies covered domains such as interaction with the eHealth system and access to the system. The mean eHEALS score in the studies ranged from 27.8 to 31.7 (8–40), indicating a higher eHealth literacy. One study reported desirable eHealth literacy based on the Digital Health Literacy Instrument. Another study reported a relatively high score on the Staff eHealth literacy questionnaire. eHealth literacy was associated with socio-demographic factors, experience of technology, health behaviour and work-related factors.ConclusionsHealth care providers have good individual eHealth literacy. However, more research is needed on the eHealth literacy domains dependent on interaction with the eHealth system and access to the system. Furthermore, most studies were conducted in Eastern and Central-Africa, and more research is thus needed in a Western context.Trial registrationPROSPERO International Prospective Register of Systematic Reviews (CRD42022363039).

The Potential of Digital Screening Tools for Childhood ADHD in School Environments: A Preliminary Study.

Attention deficit hyperactivity disorder (ADHD) is a highly prevalent developmental disorder in children. However, accurately identifying ADHD in early childhood remains a crucial challenge. Electronic health (e-health) systems offer promising possibilities to enhance the diagnostic process for ADHD, particularly concerning the executive functions (EFs) that play a direct role. This study aims to validate an evidence-based tool for screening ADHD through EFs in the school environment. The tool, named Sendero Gris, is designed for tablet devices and is based on a previously validated test with the same name. To ensure its validity, a comparison was made between the results obtained from the tool to be validated and the original format of the test. The analysis revealed no statistically significant differences between the two approaches at a 90% confidence level (p-value = 0.49). Moreover, a user experience study focusing on usability was conducted to assess the children's inclination to use the developed tool, yielding highly positive results. The implementation of Sendero Gris on a tablet device, with its objective and versatile nature, seems to maintain the potential of the original format as a screening tool for ADHD.

Fischer machine learning for mobile cloud computing in eHealth systems using blockchain mechanism

The Electronic Healthcare (eHealth) systems are competent to ensure effective care engineering and intensified healthcare quality which are user-friendly cache and administration, in Electronic Health Records (EHRs). For secure EHRs of Mobile Cloud-based eHealth systems, ensuring high security and data privacy, Interplanetary File System in healthcare has traditionally been concentrated. However, there has been a recent push towards achieving high quality of e-health services because blockchain-based health care applications require QoS guarantees in terms of requirements such as network latency and end-to-end delay. In this work, an Extended Validation Certification-based Fischer Neural Network Optimization (EVC-FNNO) method for secured Mobile Cloud-based E-Health Systems is proposed. With the identity being the digital certificate, the EVC is provided with the identity to the mobile cloud user who will transact in the network. In this way, the mobile cloud user is being ensured to access the ledger for the transaction. Therefore, both data privacy and security is said to be provided. Next, with Fischer Neural Network Optimization (FNNO), every authenticated mobile cloud user via EVC then possess a copy of shared ledger, therefore resolving data acquisition in cloud server and hence solving network latency. The proposed method is verified by some demonstrative examples in addressing QoS. The empirical results show that the EVC-FNNO method provides an efficient solution by validating the mobile cloud user sensitive health information with digital certificate. Security analysis proves that the EVC-FNNO method is secure. We also conduct comprehensive performance evaluations that demonstrate the high efficiency of the EVC-FNNO method in terms of end-to-end delay, network latency and data privacy, compared to the existing data sharing methods.

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.

Validation of the Usability Metrics for eHealth systems in Tanzania

The prevalence of eHealth systems in healthcare facilities has experienced significant growth in many countries. Nevertheless, a significant obstacle that has arisen pertains to the usability of the implemented systems. This paper focuses on assessing the usability metrics that are applicable in evaluating eHealth systems in Tanzania’s contexts. Data were collected through questionnaires and interviews at six health facilities in Tanzania. The main participants were the health workers who were using the eHealth system in their routine healthcare delivery, including pharmacists, laboratory technicians, doctors, and nurses in their different cadres. The analysis of the findings was performed to obtain usability metrics and contextual issues by computing confirmatory factor analysis (CFA) using structural equation modelling (SEM) for quantitative data and thematic analysis for qualitative data. The results revealed that 11 usability metrics constructs with 54 items and 5 contextual issues constructs with 18 items were applicable in evaluating the usability of eHealth systems in Tanzania

Clinical impact of an integrated e-health system for diabetes self-management support and shared decision making (POWER2DM): a randomised controlled trial

Aims/hypothesisThere is a lack of e-health systems that integrate the complex variety of aspects relevant for diabetes self-management. We developed and field-tested an e-health system (POWER2DM) that integrates medical, psychological and behavioural aspects and connected wearables to support patients and healthcare professionals in shared decision making and diabetes self-management.MethodsParticipants with type 1 or type 2 diabetes (aged >18 years) from hospital outpatient diabetes clinics in the Netherlands and Spain were randomised using randomisation software to POWER2DM or usual care for 37 weeks. This RCT assessed the change in HbA1c between the POWER2DM and usual care groups at the end of the study (37 weeks) as a primary outcome measure. Participants and clinicians were not blinded to the intervention. Changes in quality of life (QoL) (WHO-5 Well-Being Index [WHO-5]), diabetes self-management (Diabetes Self-Management Questionnaire – Revised [DSMQ-R]), glycaemic profiles from continuous glucose monitoring devices, awareness of hypoglycaemia (Clarke hypoglycaemia unawareness instrument), incidence of hypoglycaemic episodes and technology acceptance were secondary outcome measures. Additionally, sub-analyses were performed for participants with type 1 and type 2 diabetes separately.ResultsA total of 226 participants participated in the trial (108 with type 1 diabetes; 118 with type 2 diabetes). In the POWER2DM group (n=111), HbA1c decreased from 60.6±14.7 mmol/mol (7.7±1.3%) to 56.7±12.1 mmol/mol (7.3±1.1%) (means ± SD, p<0.001), compared with no change in the usual care group (n=115) (baseline: 61.7±13.7 mmol/mol, 7.8±1.3%; end of study: 61.0±12.4 mmol/mol, 7.7±1.1%; p=0.19) (between-group difference 0.24%, p=0.008). In the sub-analyses in the POWER2DM group, HbA1c in participants with type 2 diabetes decreased from 62.3±17.3 mmol/mol (7.9±1.6%) to 54.3±11.1 mmol/mol (7.1±1.0%) (p<0.001) compared with no change in HbA1c in participants with type 1 diabetes (baseline: 58.8±11.2 mmol/mol [7.5±1.0%]; end of study: 59.2±12.7 mmol/mol [7.6±1.2%]; p=0.84). There was an increase in the time during which interstitial glucose levels were between 3.0 and 3.9 mmol/l in the POWER2DM group, but no increase in clinically relevant hypoglycaemia (interstitial glucose level below 3.0 mmol/l). QoL improved in participants with type 1 diabetes in the POWER2DM group compared with the usual care group (baseline: 15.7±3.8; end of study: 16.3±3.5; p=0.047 for between-group difference). Diabetes self-management improved in both participants with type 1 diabetes (from 7.3±1.2 to 7.7±1.2; p=0.002) and those with type 2 diabetes (from 6.5±1.3 to 6.7±1.3; p=0.003) within the POWER2DM group. The POWER2DM integrated e-health support was well accepted in daily life and no important adverse (or unexpected) effects or side effects were observed.Conclusions/interpretationPOWER2DM improves HbA1c levels compared with usual care in those with type 2 diabetes, improves QoL in those with type 1 diabetes, improves diabetes self-management in those with type 1 and type 2 diabetes, and is well accepted in daily life.Trial registrationClinicalTrials.gov NCT03588104.FundingThis study was funded by the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement number 689444).Graphical

Evaluation of the Design and Development of the HeLe Newborn Hearing Screening Tele-Audiology Systems for the Philippines.

There is increasing interest in innovation development and management in the Philippines, especially in the last decade. In the advocacy for universal hearing health, the HeLe, "Hearing for Life'' Research Program was implemented. HeLe developed novel telehealth technologies and field tested a proof-of-concept service delivery model to improve provision of newborn hearing screening and intervention services in the Philippines. As the HeLe research period concludes, this appraisal was organized to document and assess the health information technology systems of the HeLe. The evaluation follows the elements of the Centers for Disease Control and Prevention (CDC) guidelines for evaluation of public health surveillance systems. It centers on the status of the eHealth-based components of the HeLe NHS interventions: HeLe NHS module in the Community Health Information Tracking System (CHITS) electronic medical records system, the Tele-Audiology module in National Telehealth System (NTS), and the HeLe NHS registry. The evaluation is based on interviews of key HeLe research staff and documentation review. The HeLe system has a stable, SQL-Server-based architecture. It is a secure, web-based system with clean separation of back-end database and front-end Web, using Secure Socket Layer (SSL) technology. Standardization of data via mapping ensures reliable, comparable measures. HeLe demonstrates that NHS data collected by the HeLe NHS device can be sent to, stored in, and extracted from the CHITS electronic medical record system and exchanged across platforms. Where actual patient and NHS data were available, this HeLe system is validated to be efficacious to capture and seamlessly exchange data across various eHealth platforms. These eHealth technologies are described to be at Technology Readiness Level 5, "technologies are validated in a relevant environment". The HeLe program, however, needs to address completeness in documentation as a standard practice, if only to ensure better management of risks introduced by novel eHealth systems in patient care. The CDC public health surveillance checklist used for this assessment is useful in identifying gaps in research management for the HeLe inventors. It is recommended to be incorporated to be standard and implemented early in the next iteration of the HeLe research. Overall, the HeLe technologies, in this initial stage of research, have achieved the purpose for which they were developed. As a novel technology-based NHS system, HeLe is a potentially powerful tool to assist in monitoring newborn hearing disease caseloads by community-based primary care clinics, NHS facilities, and hospitals that provide definitive medical services. As other health systems strengthening reforms take root in the Philippines, secure exchange of data electronically across the country would depend on sound technologies, including those used in hearing health. This paper can be instructive to the emerging research community in the eHealth and biomedical development space especially in resource-challenged settings. Likewise, lessons can reinforce institutional support from research agencies, clinicians, and state/ county or subnational health departments for policy and resource mobilization to better manage those identified with congenital hearing loss.

Development and feasibility of a telemedicine tool for patients with recurrent urinary tract infection: myRUTIcoach

Introduction and hypothesisPatients with recurrent urinary tract infection (rUTI) have limited knowledge of preventive strategies to lower the risk of UTI. We aimed to develop and test the feasibility of an eHealth system for women with rUTI, named myRUTIcoach, and explored the facilitators and barriers related to its adoption.MethodsWe developed myRUTIcoach in a structured iterative process and tested its feasibility among 25 women with rUTI over 2 months. Subsequent questionnaires covered satisfaction, accessibility, and experiences with myRUTIcoach. A random selection of participants and relevant stakeholders took part in semi-structured interviews to explore adoption. Data were analyzed and elaborated using inductive and deductive approaches using the Non-adoption, Abandonment, Spread, Scale-up, and Sustainability (NASSS) framework.Results MyRUTIcoach was not only widely accepted but also facilitated communication with health care professionals (HCPs) and contributed to greater knowledge of rUTI. Women graded the system a mean of 8.0 (±0.6) out of 10, with 89% stating that they would recommend it to others. Patients indicated that self-management skills were the major facilitators and barriers related to adoption, whereas HCPs stated that the disconnect between myRUTIcoach and electronic health care records (EHRs) was the major barrier.ConclusionsThis research describes the development and testing of myRUTIcoach for women with rUTI. Patients and HCPs reported high satisfaction and compliance with myRUTIcoach. However, adoption by the intended users is complex and influenced by all examined domains of the NASSS framework. We have already improved linkage to EHRs, but further optimization to meet patient needs may improve the effectiveness of this self-management tool for rUTI.

Clinical Prediction on ML based Internet of Things for E-Health Care System

Machine learning (ML) is a powerful method for uncovering hidden patterns in data from the Internet of Things (IoT). These hybrid solutions intelligently improve decision-making in a variety of fields, including education, security, business, and healthcare. IoT uses machine learning to uncover hidden patterns in bulk data, allowing for better forecasting and referral systems. IoT and machine learning have been embraced in healthcare so that automated computers may generate medical records, anticipate diagnoses, and, most critically, monitor patients in real time. On different databases, different ML algorithms work differently. The overall outcomes may be influenced by the variance in anticipated results. In the clinical decision-making process, there is a lot of variation in prognostic results. As a result, it's critical to comprehend the various machine learning methods utilised to handle IoT data in the healthcare industry. Machine learning of adaptive neuro fuzzy inference system (ANFIS) algorithms is being used to monitor human health in this suggested effort. The UCI database is used for initial training and validation of machine learning systems. Using the IoT system, the test phase collects the person's heart rate, blood pressure, and temperature. The test stage assesses if the sensor data obtained by the IoT framework can predict any irregularities in the health state. To evaluate the accuracy of the forecast %, statistical analysis is performed on cloud data acquired from the IoT. Other routines are derived from K-neighbour results.

Assessment of the Influence of Contextual Issues in Evaluating eHealth Systems Usability in Tanzania

The adoption of eHealth systems in healthcare facilities has rapidly increased in many countries. However, the main challenge has been the quality and level of usability of those adopted systems, and the complexity of the challenge varies from one country to another based on contextual issues. Usability is also linked to contextual issues, which have a direct impact on deciding the methods for providing services on similar systems. This research focuses on assessing the influence of contextual issues on metrics that are applicable for evaluating the usability of eHealth systems through a case study of Tanzania. The findings of this research were obtained through the quantitative method, and the analysis was performed through structural equation modelling (SEM) using AMOS and SPSS applications. It is evident from this research that contextual factors, such as user characteristics, goals and tasks, technical environment, physical environment, and resources and technology, significantly impact the usability metrics that are important to the evaluation of eHealth systems. The usability metrics for eHealth systems identified in this research include navigation visibility, accessibility, perceived ease of use, error correction, internal collaboration, information quality, external collaboration, technical quality, guide and support, and perceived benefits. This study also revealed that the usability evaluation of eHealth systems should consider both common metrics and specific metrics in order to uncover both general information system and health context-specific usability issues

Predictors of the Use of a Mental Health-Focused eHealth System in Patients With Breast and Prostate Cancer: Bayesian Structural Equation Modeling Analysis of a Prospective Study.

eHealth systems have been increasingly used to manage depressive symptoms in patients with somatic illnesses. However, understanding the factors that drive their use, particularly among patients with breast and prostate cancer, remains a critical area of research. This study aimed to determine the factors influencing use of the NEVERMIND eHealth system among patients with breast and prostate cancer over 12 weeks, with a focus on the Technology Acceptance Model. Data from the NEVERMIND trial, which included 129 patients with breast and prostate cancer, were retrieved. At baseline, participants completed questionnaires detailing demographic data and measuring depressive and stress symptoms using the Beck Depression Inventory-II and the Depression, Anxiety, and Stress Scale-21, respectively. Over a 12-week period, patients engaged with the NEVERMIND system, with follow-up questionnaires administered at 4 weeks and after 12 weeks assessing the system's perceived ease of use and usefulness. Use log data were collected at the 2- and 12-week marks. The relationships among sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness (PU), and system use at various stages were examined using Bayesian structural equation modeling in a path analysis, a technique that differs from traditional frequentist methods. The path analysis was conducted among 100 patients with breast and prostate cancer, with 66% (n=66) being female and 81% (n=81) having a college education. Patients reported good mental health scores, with low levels of depression and stress at baseline. System use was approximately 6 days in the initial 2 weeks and 45 days over the 12-week study period. The results revealed that PU was the strongest predictor of system use at 12 weeks (βuse at 12 weeks is predicted by PU at 12 weeks=.384), whereas system use at 2 weeks moderately predicted system use at 12 weeks (βuse at 12 weeks is predicted by use at 2 weeks=.239). Notably, there were uncertain associations between baseline variables (education, sex, and mental health symptoms) and system use at 2 weeks, indicating a need for better predictors for early system use. This study underscores the importance of PU and early engagement in patient engagement with eHealth systems such as NEVERMIND. This suggests that, in general eHealth implementations, caregivers should educate patients about the benefits and functionalities of such systems, thus enhancing their understanding of potential health impacts. Concentrating resources on promoting early engagement is also essential given its influence on sustained use. Further research is necessary to clarify the remaining uncertainties, enabling us to refine our strategies and maximize the benefits of eHealth systems in health care settings.

INVESTIGATION ANALYSIS ON E-HEALTHCARE MONITORING SYSTEM WITH IOT AND BIG DATA BY TSLR-CHSC MODEL

Internet of Things (IoT) technology helped the development of E-Health care Monitoring System from direct visit to virtual Monitoring (Telemedicine). Smart health care system in IoT environment monitored the basic health signs such as heartbeat rate, body temperature and blood pressure in real-time applications. IoT and big data is a prominent challenge in smart healthcare system. In this health monitoring system big data is employed to analyze the large volume of data and to determine the Normal and Abnormal patient condition. Numerous issues like accuracy, time and error have yet to be conveyed to generate a ductile system for health care monitoring. To address these issues, I proposed a method called Theil Sen Linear Regression and Canopy Hopkins Statistics Clustering (TSLR-CHSC) for IoT based Health monitoring system is proposed. This method splits into three sections such as Data collection, Feature Selection and Clustering. First, Cardiovascular disease dataset is acquired from sensors are collected. Second, appropriate features can be selected by using Theil Sen regression feature. Third, clustering is performed based on the cluster tendency by using canopy algorithms. Through this way I deployed an efficient E-Health Monitoring System with minimum time consumption. For evaluation, a cardiovascular disease dataset is obtained from various medical sensor devices are analyzed to identify the disease severity. Keyword: IoT, Big Data, Theil Sen Linear Regression, Canopy Hopkins Statistic Clustering, Health Care Monitoring.

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

Machine learning-based D2D communication for a cloud-secure e-health system and data analysis by feature selection with classification

FEeSU - A Framework for Evaluating eHealth Systems Usability: A Case of Tanzania Health Facilities

FEeSU - A Framework for Evaluating eHealth Systems Usability: A Case of Tanzania Health Facilities