Abstract

Introduction
Child morbidity and mortality in resource-limited settings is a major public health problem. The previous studies were mainly concerned with determining the prevalence of child deaths and identifying associated factors. Extracting knowledge and discovering insights from hidden patterns in child data through supervised machine learning algorithms is limited. Therefore, this study aimed to predict the under-five death of children using a best performance-supervised machine learning algorithm.

Methods
A total of 1813 samples were used from the 2019 Ethiopian Demographic and Health Survey dataset. 70% and 30% of total instances were used for training the model and measuring the performance of each algorithm with 10-fold cross-validation techniques respectively. Five supervised machine learning algorithms were considered for model building and comparison. All the included algorithms were evaluated using confusion matrix elements. Information gain value was used to select important attributes to predict child deaths. The If/then logical association was used to generate rules based on relationships among attributes using Weka version 3.8.6 software.

Results
J48 is the second-best performance algorithm next to the random forest to predict child death, with 77.8% and 93.9% accuracy, respectively. Late initiation of breastfeeding, mothers with no formal education, short birth intervals, poor wealth status of the mother, and unexposed to media were the top five important attributes to predict child deaths. A total of six associated rules were generated that could determine the magnitude of child deaths. Of these, if children were rural residents, had a short birth interval, and if born as multiples (twins), then the probability of child death was 83.6%.

Conclusions
Five machine learning algorithms were included to predict child deaths and generate rules. Hence, the random forest algorithm was the best algorithm to predict child deaths. However, the study was limited since important attributes were not included in the data source, and irrelevant values were found. So, researchers are encouraged to use machine learning algorithms for future studies including important attributes that could predict child death. The current findings would be useful for stakeholders’ preparedness, and taking proactive childcare interventions. Encouraging women in education, media access, and economic development programs are essential interventions for child death reduction.

Introduction

Under-five mortality is the most important indicator to measure the health status of children, and it is a key marker for the development of countries [1]. The under-five mortality rate is the probability of children dying before their fifth birthday [2]. Globally, nearly 44% of all under-five deaths occurred before their first month of birth [3], and an estimated 4.1 million child deaths occurred in 2017 [4]. According to the Centers for Disease Control and Prevention, child mortality in the United States in 2020 was predicted to be 5.4 deaths per 1000 live births [5].

The risk of under-five mortality is highest in low-income countries. The under-five mortality rate in low-income countries was predicted to be 69 deaths per 1000 live births in 2017, which is almost 14 times the rate in high-income countries [6,7]. In Bangladesh, 522 under-five children died per 1000 live births [8]. In 2001, under-five mortality in Nepal was projected to be 91 deaths per 1000 live births [9]. Though under-five mortality shows a reduction from 166 to 67 per 1000 live births over a period of 16 years [10], Ethiopia appears to have the fifth-highest number of new-born deaths in the world [11]. Under-five mortality is projected to cause 472,000 children to die annually in Ethiopia before their fifth birthday, which places Ethiopia sixth in the world according to the number of under-five deaths [7,12]. According to WHO 2017, more than half of under-five deaths are due to infectious diseases that are easily preventable and treatable through simple and affordable interventions [13]. Under-five mortality is also caused by undernutrition, which further leads to stunting and wasting [14].

Conclusions and recommendations

This study aimed to identify the best-supervised machine learning algorithms to classify and select important attributes to predict the death of children before their fifth birthday. In line with the objectives, six supervised machine learning algorithms were considered that accurately predict the death of children before celebrating their fifth birthday. Different confusion matrix element was used to compare the candidate-supervised machine learning algorithms. Based on the result, the random forest algorithm was the best performance model to predict the death of children before celebrating their fifth birthday. Attributes such as late initiation of breastfeeding, mothers with no formal education, the short birth interval, poor wealth status of the mother, and being unexposed to media were the top important attributes to predict child deaths.

Generating associated rules for child death was another objective of the study. Accordingly, six rules were generated that were associated with the deaths of children before celebrating their fifth birthday. The findings of this study would have practical implications by supporting policymakers and stakeholders in developing childcare intervention mechanisms and preparing themselves to care for children as early as possible. Stakeholders are recommended to encourage mothers to initiate breastfeeding at the appropriate time. Improving mothers’ wealth status, closing the gap in media access, and creating awareness among mothers would be critical interventions to enhance the survival of children in Ethiopia. The generated rules would also have theoretical implications by extracting and representing knowledge. Moreover, researchers would use this study as a baseline and framework for further research studies, including important attributes that would predict child mortality in low-income countries.

Abstract

     The increased accuracy and exhaustivity of modern Artificial Intelligence techniques in supporting the analysis of complex data, such as medical images, have exponentially increased real-world data collection for research purposes. This fact has led to the development of international repositories and high-performance computing solutions to deal with the computational demand for training models. However, other stages in the development of medical imaging biomarkers do not require such intensive computing resources, which has led to the convenience of integrating different computing backends tailored for the processing demands of the various stages of processing workflows. We present in this article a distributed and federated repository architecture for the development and application of medical image biomarkers that combines multiple cloud storages with cloud and HPC processing backends. The architecture has been deployed to serve the PRIMAGE (H2020 826494) project, aiming to collect and manage data from paediatric cancer. The repository seamlessly integrates distributed storage backends, an elastic Kubernetes cluster on a cloud on-premises and a supercomputer. Processing jobs are handled through a single control platform, synchronising data on demand. The article shows the specification of the different types of applications and a validation through a use case that make use of most of the features of the platform.

Introduction

     Increasingly, radiology is based on objective and quantifiable data extracted from Quantitative Imaging Biomarkers (QIBs). QIBs are quantitative indicators generated from structural, functional, physiological or biological characteristics of pathological lesions [1]. In the workflow development of QIBs, complex computational functions and models automatically extract attributes, namely radiomics features, from different types of radiological images to correlate them to the phenotype or genetic signatures of the lesions. These analyses aim to early detect and classify anomalies to predict prognostics, define follow-up results, or non-invasively assess the treatment response.

     In the last years, developers have analysed the images by learning from retrospective data, enriching radiomics features with demographic, clinical, liquid biopsies and genomic data because they improve the clinical value of the biomarkers [2]. Thus, gathering data processes are crucial to developing useful Clinical Decision support Systems (CDSS) based on QIBs in clinical practice, requiring a massive storage and high-performance computing capacity [3] for managing data on image biobanks. Furthermore, the huge amount of data makes traditional statistical analyses impractical, leading to a transition to novel textitArtificial Intelligence (AI) solutions such as Deep Learning [4]. Running AI algorithms efficiently requires high-computing performance resources [5] connected to the data storage backends.

Conclusions

     This work describes the design, implementation and validation of a software architecture to support the development and application of Quantitative Image Biomarkers. It implement a federated model to synchronise data among the different storage backends linked to different processing environments, including both Cloud and HPC resources. The architecture provides a federated Authentication and Authorisation Infrastructure based on Virtual Organisations that provide coherent and scalable authorisation management across the different providers. The processing backend is supported by a Kubernetes container management platform that runs the platform services and customised applications. The architecture is the outcome of a requirement elicitation process and uses mainstream, widely available components.

      The architecture uses the abstraction of the batch, High-Throughput-Compute, High-Performance computing and Interactive jobs to provide a simplified framework to develop applications with POSIX access to the distributed storage backends. HPC Jobs are managed through a mirror Kubernetes job that interacts with HPC batch queues to provide a seamless and coherent environment.

Highlights

Abstract

Keywords

1. Introduction

2. Theoretical background

3. Conceptual model and hypothesis development

4. Methodology

5. Data analysis

6. Discussion

7. Conclusion

Authorship statement

Declaration of Competing Interest

Acknowledgements

Appendix A: Study measurement

References

Vitae

Abstract

There are numerous risks associated with the interconnection of healthcare provision and the Internet of Things (IoT), with its sensory capabilities shown to reduce confidence in novel technology due to fears of a loss of privacy. There exists a clear omission in the extant literature—consideration of gender differences in Frontline Healthcare Providers’ (FHP) behavioural intentions—which this work aims to address through the analysis of IoT-enabled healthcare applications’ (HAs) behavioural intentions in multicultural and bi-generational (Gen X, Y) context. Essentially, analysing gender and generational differences in relation to the variables (privacy, security and trust that influence risk perception; the latter alongside attitude and perceived behavioural control potentially affect the intention) affecting FHPs’ BI towards IoT enabled HAs. A novel model is presented herein, which combines Planned Behaviour (TPB), Privacy Calculus (PCT), and the trust-risk framework. Questionnaire methodology (n = 401) was applied to both generations under consideration, data was assessed using Partial Least Squares Multi-Group Analysis (PLS-MGA), which showed gender differences in Gen Y, but there was little evidence to suggest that risk perception affects any of the cohort’s behavioural intention towards the use of IoT-enabled HA, which in turn should help guide both future institutional policy and application development .

1. Introduction

Advances in network technologies, combined with mass production of smart devices equipped with sensors with continuous, bidirectional transmission, and the advent of cloud computing have been the primary drivers behind IoT development and implementation in big data driven infrastructural control (Hassan et al., 2018; Li et al., 2020; Rafique et al., 2020; Razzak et al., 2020). However, the security problems inherent in the wider internet itself remain prevalent in IoT. In truth, each element in IoT’s tri-layer structure—perception, transport, and application—requires individual consideration in that respect (Tewari and Gupta, 2020). IoT is now commonplace in modern society, often appearing in the food supply chain, logistics, mining, computing, and healthcare sectors (Pang et al., 2015; Yildirim and Ali-Eldin, 2019). Particularly in the case of the latter, the drive for service improvement has resulted in a broad body of literature considering this advance (Asif-Ur-Rahman et al., 2019; Syed et al., 2019).

The interconnected nature of the IoT enabled healthcare model (Tewari and Gupta, 2020) contains a multitude of risks relating to privacy, security and loss of trust. Limitations on the computational ability of this model means that conventional measures used to tackle security and privacy concerns often cannot be applied (Li et al., 2020), leading to the development of a concept known as the Internet of Medical Things, wherein patient data confidentiality without loss of functionality is held paramount (Li et al., 2020).

Abstract

Keywords

1. Introduction

2. Sensors

3. Wireless Communication Technologies

4. Cloud Computing

5. Machine Learning Techniques

6. Fog/Edge Computing

7. Applications

8. Conclusion

Declaration of Competing Interest

References

Vitae

Abstract

The goal of Internet of Medical Things (IoMT) and digital healthcare systems is to provide people with the ease of receiving quality healthcare at the comfort of their homes. Hence, the aim of IoMT is the ubiquitous deployment of home-based healthcare systems. Making such systems intelligent and efficient for timely prediction of critical diseases can save millions of lives while simultaneously reducing the burden on the traditional healthcare systems e.g., hospitals. The advancement in IoT has enabled both patients and doctors to access real time data. This advancement has reduced the cost and energy consumption of digital healthcare systems by using efficient sensors and communication technologies. This paper provides a comprehensive review of various studies conducted for the development and improvement of IoMT. It analyses different sensors used for measurement of various parameters ranging from physiological to emotional signals. It also provides a detailed investigation of different communication technologies being used, their advantages, and limitations. Moreover, digital healthcare systems are now deploying machine learning technology for the prediction of health status of patients. These techniques and algorithms are also discussed. Data security and prediction accuracy are the main concerns in the development of this area. In conclusion, this paper reviews the various digital system designs in the context of healthcare, their methodology, limitations, and the present challenges faced by the e-health sector.

1. Introduction

Quality healthcare is a basic human right, but one which fails to be provided adequately worldwide. The economic, environmental, and social development of this world and subsequent lifestyle changes have led to a drastic increase in chronic diseases such as heart disease, cancer, and diabetes. These chronic illnesses symbolize the greatest threat to human health. Moreover, each time an infectious disease breaks out, the hospitals are flooded with people which takes a huge toll on healthcare services. For example, currently there is a continuous stress on the worlds healthcare resources with the rampant spread of COVID-19. This kind of situation leads to inefficiency in managing patients and their data.

Experts believe that digital healthcare systems in Internet of Things (IoT) environments seem to be a compelling solution to this major healthcare problem. The building blocks and general architecture of a system in the IoT environment is shown in Fig. 1. In the context of medical services, the traditionally proposed remote health monitoring system architectures are divided into three layers: the vitals data collection layer from sensors; the transmission layer; and the analysis layer. The collection layer consists of sensors in the body area network (BAN). BAN collects the sensor data and transmits it to a gateway node. The transmission layer stores that data and analyzes it using conventional threshold values to report any abnormality. Additionally, the data may also be sent to the cloud for storage and heavy computations.

1- Introduction

2- Materials and methods

3- Results

4- Discussion

5- Conclusion

References

Abstract

Gamma rhythms (~20–70 ​Hz) are abnormal in mental disorders such as autism and schizophrenia in humans, and Alzheimer’s disease (AD) models in rodents. However, the effect of normal aging on these oscillations is unknown, especially for elderly subjects in whom AD is most prevalent. In a first large-scale (236 subjects; 104 females) electroencephalogram (EEG) study on gamma oscillations in elderly subjects (aged 50–88 years), we presented full-screen visual Cartesian gratings that induced two distinct gamma oscillations (slow: 20–34 ​Hz and fast: 36–66 ​Hz). Power decreased with age for gamma, but not alpha (8–12 ​Hz). Reduction was more salient for fast gamma than slow. Center frequency also decreased with age for both gamma rhythms. The results were independent of microsaccades, pupillary reactivity to stimulus, and variations in power spectral density with age. Steady-state visual evoked potentials (SSVEPs) at 32 ​Hz also reduced with age. These results are crucial for developing gamma/SSVEP-based biomarkers of cognitive decline in elderly.

Introduction

Gamma rhythms are narrow-band oscillations often observed in the electrical activity of the brain, with center frequency occupying ~20-70 Hz frequency range. Previous studies have proposed involvement of these rhythms in certain higher cognitive functions like feature binding (Gray et al., 1989), attention (Chalk et al., 2010; Gregoriou et al., 2009) and working memory (Pesaran et al., 2002). Further, some studies have shown that these rhythms may be abnormal in neuropsychiatric disorders such as schizophrenia (Hirano et al., 2015; Tada et al., 2014), autism (An et al., 2018; Uhlhaas and Singer, 2007; Wilson et al., 2007) and Alzheimer’s disease (Mably and Colgin, 2018; AD; Palop and Mucke, 2016). Gamma rhythms can be induced in the occipital areas by presenting appropriate visual stimuli such as bars and gratings, and their magnitude and center frequency critically depend on the properties of the stimulus such as contrast, size, orientation, spatial frequency and drift rate (Jia et al., 2013; Murty et al., 2018; Ray and Maunsell, 2015). Recently, we showed that large (full-screen) gratings induce two distinct narrow-band gamma oscillations in local field potentials (LFP) in macaque area V1 and posterior electrodes in human EEG, which we termed slow (~20-40 Hz) and fast (~40-70 Hz) gamma (Murty et al., 2018).

1- Introduction

2- Methods and materials

3- Results

4- Discussion

5- Conclusion

References

Abstract

Atrial fibrillation (AF) is a common cardiac arrhythmia in clinic. The traditional AF detection using visual inspection by trained physicians is an inefficient and burdensome task. In this paper, we introduce a novel method for the automated AF detection using two-lead electrocardiogram (ECG) signals. We use the wavelet packet transform (WPT) and the correlation function of random process theory to devise an efficient feature extraction strategy for physiological signal analysis, and construct the corresponding histogram. Then, multivariate statistical features based on the correlation among wavelet coefficient series are extracted from the corresponding histogram as the feature set, which is the input to artificial neural network (ANN) classifier for the detection. Moreover, various statistical analyses are performed and some parameter tuning strategies are formulated by fitting the receiver operating characteristic (ROC) curve to ensure the reliability and robustness of the work. To evaluate the classification performance of the algorithm, 10-fold cross-validation is implemented on the MIT-BIH AF database. Compared with some state-of-the-art algorithms, the numerical results prove that our proposed strategy yields superior classification performance. To the best of our knowledge, this is also the first application of random process theory for AF detection, providing great potential in medical diagnosis.

Introduction

Atrial fibrillation (AF), a common cardiac arrhythmia, endangers human health and causes a social burden, which is closely related to the occurrence of stroke, thrombus, even death. More than 6 million people have been affected in the United States and about 90 million people have AF around the world [1]. This trend is expected to increase significantly with age [2]. Therefore, it is essential for public health to combat the harm and prevalence of the disease. Conventionally, AF detection is diagnosed by visual inspection of electrocardiogram (ECG) signals by trained physicians, whichmakes artificialdetectioninefficient andsubjective [3]. A large amount of ECG data have inevitably hindered the efficiency of AF detection. Hence,there is an urgent demand for an automated AF detection mechanism to analyze massive amounts of ECG data, facilitate diagnosis and lighten the burden on physicians [4]. The ECG is one of the most common and powerful tools for diagnosing atrial activity in clinicaltreatment, which mainly reveals the electrical action in the heart of human body [3,5]. The irregularity of RR intervals and absence of P-waves (replaced by rapid, irregular and disordered fibrillatory waves, called f-waves) are two main features of ECG data in AF [6]. Various algorithms based on these two features have been performed to automatically detect AF from ECG data. According to the absence of P-waves, Maji et al. [7] utilized empirical mode decomposition to analyze the denoised ECG signals for the corresponding intrinsic mode functions, and then some statistical parameters of P-waves were derived from these functions to perform classification. Padmavathi and Ramakrishna [8] measured some related coefficients based on the auto-regressive model of ECG signals and the support vector machine (SVM) as well as K-nearest neighbor (KNN) classifier was used to detect AF episodes. Pourbabaee et al.[9] developed a feature learning method using deep convolutional neural networks for the analysis of ECG data. At the same time, more schemes using the irregularity of RR intervals have also been initiated to classify AF signals. Dash et al.

1- Introduction

2- Feature extraction

3- Epileptic seizure detection

4- Methods for feature evaluation

5- Experimental results

6- Conclusions

References

Abstract

Since the manual detection of electrographic seizures in continuous electroencephalogram (EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop automatic seizure detection are diverse and ongoing. Machine learning approaches are intensely being applied to this problem due to their ability to classify seizure conditions from a large amount of data, and provide pre-screened results for neurologists. Several features, data transformations, and classifiers have been explored to analyze and classify seizures via EEG signals. In the literature, some jointly-applied features used in the classification may have shared similar contributions, making them redundant in the learning process. Therefore, this paper aims to comprehensively summarize feature descriptions and their interpretations in characterizing epileptic seizures using EEG signals, as well as to review classification performance metrics. To provide meaningful information of feature selection, we conducted an experiment to examine the quality of each feature independently. The Bayesian error and non-parametric probability distribution estimation were employed to determine the significance of the individual features. Moreover, a redundancy analysis using a correlation-based feature selection was applied. The results showed that the following features – variance, energy, nonlinear energy, and Shannon entropy computed on a raw EEG signal, as well as variance, energy, kurtosis, and line length calculated on wavelet coefficients – were able to significantly capture the seizures. When compared with a baseline method of classifying all epochs as normal, an improvement of 4.77–13.51% in the Bayesian error was obtained.

Introduction

An epileptic seizure, as defined by the International League Against Epilepsy [1], is a temporary event of symptoms due to synchronization of abnormally excessive activities of neurons in the brain. It has been estimated that approximately 65 million people around the world are affected by epilepsy [2]. Nevertheless, it is still a time-consuming process for neurologists to review continuous electroencephalograms (EEGs) to monitor epileptic patients. Therefore, several researchers have developed differenttechniques that help neurologists to identify an epilepsy occurrence [3–5]. The whole process of automated epileptic seizure analysis primarily consists of data acquisition, signal pre-processing, feature extraction, feature or channel selection, and classification. This paper focuses on a selection of features commonly used in the literature, including statistical parameters (mean, variance, skewness, and kurtosis), amplitude-related parameters (energy, nonlinear energy, line length, maximum and minimum values) and entropyrelated measures. These features can be categorized based on their interpretation or the domain from which the features are calculated. While some studies have considered a particular group of features applicable to their proposed classification method [6–8], others have applied various groups of features extracted from the time, frequency, and time-frequency domains. For example, 55 features were used with a support vector machine (SVM) and postprocessing for neonatal seizure detection, which provided a good detectionrate of 89.2% withone false detectionperhour [9]. Feature redundancy and relevance analysis were applied to 132 features to reduce the vector dimension [10]. Fast correlation based-filter proposed in [11], correlation-based selection (CFS) [12], and ReliefF established in [13], were utilized to select non-redundant features and the filtered features were classified by an artificial neural network (ANN). It turned out that 30-optimal selected features by ReliefF achieved the best result with 91% sensitivity and 95% specificity. These studies showed thatthere is the need to review feature selection as relevant features are directly related to the seizure classification performance.

Introduction

Basic definitions

Patient Intake: obtaining initial patient data

Radiology

Hematology

Neurology

Oncology

Cell biology and cell therapy

Cardiology

Ophthalmology

Conclusion

Declaration of Competing Interest

Acknowledgments

References

Abstract

Motivation: As a result of the worldwide health care system digitalization trend, the produced healthcare data is estimated to reach as much as 2314 Exabytes of new data generated in 2020.

The ongoing development of intelligent systems aims to provide better reasoning and to more efficiently use the data collected. This use is not restricted retrospective interpretation, that is, to provide diagnostic conclusions. It can also be extended to prospective interpretation providing early prognosis. That said, physicians who could be assisted by these systems find themselves standing in the gap between clinical case and deep technical reviews. What they lack is a clear starting point from which to approach the world of machine learning in medicine.

Methodology and Main Structure: This article aims at providing interested physicians with an easy-tofollow insight of Artificial Intelligence (AI) and Machine Learning (ML) use in the medical field, primarily over the last few years.

To this end, we first discuss the general developmental paths concerning AI and ML concept usage in healthcare systems. We then list fields where these technologies are already being put to the test or even applied such as in Hematology, Neurology, Cardiology, Oncology, Radiology, Ophthalmology, Cell Biology and Cell Therapy.

Introduction

The introduction of information technology in the field of healthcare has provided improvements on numerous aspects [1], starting from digitization of patient data in electronic health records (EHR) [2] to providing clinical decision making [3]

As a result of the worldwide health care system digitalization trend,the produced healthcare data in 2011 have been estimated to be 150 Exabytes 150 * 10^18, and it is estimated to have 2314 Exabytes of newly produced data in 2020 [4,5]. However, processing these data efficiently so that useful information and new knowledge can be extracted remains a real challenge. In fact, the ever-increasing amount of collected data withstands the ability of current data analysis systems. As a result, healthcare systems are increasingly burdened. This is called the “Data Rich/Information Poor (DRIP)” syndrome [6]. DRIP means that we are collecting more data than we can analyze. Fortunately, with the latest advancements in data analysis and decision-making systems, overcoming this challenge seems to finally be feasible.

Software Defined Networking (SDN) has focused enormous attractiveness in changing conventional network by means of offering flexible and dynamic network management. It has drawn important concentration of the researchers from together academia and industries. Mainly, integrating SDN in Wireless Body Area Network (WBAN) applications specifies capable results in terms of handling with the issues like traffic management, security, energy efficiency etc. Recent improvements in miniaturization and energy efficient physiological sensor designs in SDN based Wireless Body Area Networks (WBANs) paved the way for health monitoring systems for collection and processing the real-time physiological data. The collection of signals from different sensor allows reliable diagnosis in heterogeneous than in homogeneous WBANs. Inspired by the evolutions of heterogeneous WBANs, a study on Wireless Electroencephalography Sensor Networks (WESNs) is carried out under distributed signal processing. The distributed WESNs are designed under two different hierarchy i.e. Hierarchical Fully-Connected Topology (HFCT) and Ad-Hoc Nearest-Neighbor Topology (ANNT) to improve the energy-efficiency using distributed Multi-channel Weighted Weiner Filter design (MW2F). Here, each module transmits linear combination of local channels with other modules. The power efficiency is improved Journal Pre-proof in MW2F signal processing algorithm by avoiding centralization of EEG data. A case study is carried out to test the reduced energy consumption after the removal of eye blink artifacts and it is tested with centralized counterparts. The MW2F is evaluated in both topologies against centralized environments and significant reduction of eye blink artifacts improves the energy efficiency in HFCT than other topologies.

Introduction

The energy efficient and miniaturization is the most hot research area in health monitoring systems, where the body is placed with several sensor nodes. These nodes can collect the EEG data and process it in real time and measures are considered to improve the energy efficiency. This system is called as Wireless Body Area Networks (WBANs) [1], and it offers higher demand in the area of physiological monitoring in hospital environment. The WBAN system is composed of several nodes with physiological sensors, wireless communication devices and a signal processing unit. These nodes can communicate with other sensor nodes placed over the body or it can be connected to a fusion center or a storage device. The signals collected from various resources are analyzed jointly in heterogeneous WBANs in real time environment for medical diagnosis.