Handbook of Data Science Approaches for Biomedical Engineering

By Valentina Emilia Balas, Vijender Kumar Solanki and Raghvendra Kumar

Handbook of Data Science Approaches for Biomedical Engineering covers the research issues and concepts of biomedical engineering progress and the ways they are aligning with the latest technologies in IoT and big data. In addition, the book includes various real-time/offline medical applications that directly or indirectly rely on medical and information technology. Case studies in the field of medical science, i.e., biomedical engineering, computer science, information security, and interdisciplinary tools, along with modern tools and the technologies used are also included to enhance understanding.

Today, the role of Big Data and IoT proves that ninety percent of data currently available has been generated in the last couple of years, with rapid increases happening every day. The reason for this growth is increasing in communication through electronic devices, sensors, web logs, global positioning system (GPS) data, mobile data, IoT, etc.

• Provides in-depth information about Biomedical Engineering with Big Data and Internet of Things
• Includes technical approaches for solving real-time healthcare problems and practical solutions through case studies in Big Data and Internet of Things
• Discusses big data applications for healthcare management, such as predictive analytics and forecasting, big data integration for medical data, algorithms and techniques to speed up the analysis of big medical data, and more

• Language: English
• Publisher: Academic Press
• Release date: Nov 13, 2019
• ISBN: 9780128183199

Book preview

Handbook of Data Science Approaches for Biomedical Engineering

Editors

Valentina Emilia Balas

Vijender Kumar Solanki

Raghvendra Kumar

Manju Khari

Table of Contents

Cover image

Title page

Copyright

Contributors

Chapter 1. Analysis of the role and scope of big data analytics with IoT in health care domain

1. Introduction

2. Sources of health care data

3. Tools and data analytics interfaces in medical and health care system

4. Health care with big data challenges

5. IoT defined

6. IoT for health care

7. Challenges for IoT in health care

8. Evolution of big data in medical IoT

9. Advantages

10. Literature survey

11. Implementation of a real-time big data analytics of IoT-based health care monitoring system

12. Conclusion

Chapter 2. Automated human cortical bone Haversian canal histomorphometric comparison system

1. Introduction

2. Sample collection

3. Sample preparation

4. Difficulties in sample preparation

5. Image acquisition

6. Microstructural parameter selection

7. Inclusion and exclusion criteria

8. Statistical tests

9. Automated comparison system

10. Automated system design

11. Sex comparison without age groups

12. Race comparison without age groups

13. Conclusion

Chapter 3. Biomedical instrument and automation: automatic instrumentation in biomedical engineering

1. Introduction

2. Biomedical instrumentation

3. Automation in the field of biomedical instrumentation

4. Automation in telerobotic surgeries

5. Types of robotic surgeries

6. Applications

7. Automatic wireless sensor networking in biomedical instrumentation

8. Biomedical applications of wireless sensor networking

9. Network topology

10. Bluetooth communication

11. Sensing technologies

12. Selecting RF transceivers

13. Recent advancements and applications in biomedical instrumentation

14. Conclusion

Chapter 4. Performance improvement in contemporary health care using IoT allied with big data

1. Introduction

2. Conclusion

Chapter 5. Emerging trends in IoT and big data analytics for biomedical and health care technologies

1. Introduction

2. Big data workflow for biomedical image analysis

3. Role of artificial intelligence and robotics in telemedicine

4. Wearable devices and IoT

5. Biotechnological advances

6. Conclusion

Chapter 6. Recent advances on big data analysis for malaria prediction and various diagnosis methodologies

1. Introduction

2. Disease prediction model based on big data analysis

3. Diagnosis techniques

4. Discussion

5. Conclusion

Chapter 7. Semantic interoperability in IoT and big data for health care: a collaborative approach

1. Introduction

2. State of the art

3. Semantic interoperability

4. Semantic interoperability in IoT health care

5. SI in big data health care

6. Conclusion and future work

Chapter 8. Why big data, and what it is: basics to advanced big data journey for the medical industry

1. Introduction

2. Why big data?

3. Health care and the four Vs of big data

4. An architecture of large-scale platform to develop a predictive model

5. The model through big data analytics

6. Impact of big data

7. Ethical issues

8. Conclusion

Chapter 9. Semisupervised fuzzy clustering methods for X-ray image segmentation

1. Introduction

Part 1: Theory background

1.1. Image segmentation problem

1.2. Data clustering

1.3. Fuzzy clustering

1.4. Semisupervised fuzzy clustering

Part 2: The combination of eSFCM and OTSU in image segmentation

2.1. The general diagram of the integration between the eSFCM and OTSU

2.2. OTSU threshold algorithm in image processing

Part 3: Semisupervised fuzzy clustering with spatial feature

3.1. The general framework

3.2. Determining suitable additional information

3.3. The semisupervised fuzzy clustering algorithm (SSFC-SC)

3.4. Fuzzy satisficing method and semisupervised clustering method in segmentation problem (SSFC-FS [35])

3.5. The properties and consequences from solution analysis

3.6. The advantages of the proposed algorithms

Part 4: Defining the suitable additional information for SSFC-FS algorithm

4.1. The framework of the SSFC-FSAI method

4.2. The set of additional information functions

4.3. Defining an appropriate additional information

4.4. Advantages of the new algorithm

Part 5: The results of implementations and applications

5.1. Dental X-ray image dataset

5.2. The performance among segmentation methods

2. Conclusions

Index

Copyright

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

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ISBN: 978-0-12-818318-2

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Contributors

Muhammad Mahadi Abdul Jamil,     Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia

Hadi Abdullah,     Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia

Sivadi Balakrishna,     Department of CSE, Pondicherry Engineering College, Pondicherry University, Puducherry, India

Amit Banerjee,     Microelectronic Technologies & Devices, Department of Electrical and Computer Engineering, National University of Singapore, Singapore

Debabrata Biswas,     NUS-HUJ-CREATE Molecular Mechanism of Inflammation and Disease, Department of Microbiology and Immunology, National University of Singapore, Singapore

Chinmay Chakraborty,     Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Jharkhand, India

R. Chandrasekaran,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

Salam Shuleenda Devi,     National Institute of Technology Mizoram, Aizawl, India

Arijit Dutta,     KIIT University, Bhubaneshwar, Odisha, India

Cu Nguyen Giap,     ThuongMai University, Hanoi, Vietnam

R.J. Hemalatha,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

A. Josephin Arockia Dhivya,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

A. Keerthana,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

Ijaz Khan,     Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia

Anand Kumar,     School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Rabul Hussain Laskar,     National Institute of Technology, Silchar, Assam, India

Sushruta Mishra,     KIIT University, Bhubaneshwar, Odisha, India

Brojo Kishore Mishra,     C. V. Raman College of Engineering, Bhubaneshwar, Odisha, India

Meena Moharana,     School of Computer Engineering, KIIT University, Bhubaneswar, India

Tran Thi Ngan,     Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

Faridah Mohd Nor,     Department of Pathology, Faculty of Medicine Universiti Kebangsaan Malaysia (UKM), Medical Centre, Kuala Lumpur, Malaysia

Manjusha Pandey,     School of Computer Engineering, KIIT University, Bhubaneswar, India

Mamata Rath,     Birla School of Management (IT), Birla Global University, Bhubaneswar, India

Siddharth Swarup Routaray,     School of Computer Engineering, KIIT University, Bhubaneswar, India

K. Sangeethapriya,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

Vijender Kumar Solanki,     CMR Institute of Technology (Autonomous), Hyderabad, India

Le Hoang Son,     VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

G. Srividhya,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

T.R. Thamizhvani,     Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced studies, (Deemed to be University), Chennai, India

M. Thirumaran,     Department of CSE, Pondicherry Engineering College, Pondicherry University, Puducherry, India

Do Nang Toan,     VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

Hrudaya Kumar Tripathy,     KIIT University, Bhubaneshwar, Odisha, India

Tran Manh Tuan,     Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

Mohd Helmy Bin Abd Wahab

Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia (UTHM), BatuPahat, Malaysia

Green ICT Research Group, Centre of Excellence Geopolymer and Green Technology, Universiti Malaysia Perlis, Perlis, Malaysia

Chapter 1

Analysis of the role and scope of big data analytics with IoT in health care domain

Sushruta Mishra ¹ , Brojo Kishore Mishra ² , Hrudaya Kumar Tripathy ¹ , and Arijit Dutta ¹       ¹ KIIT University, Bhubaneshwar, Odisha, India      ² C. V. Raman College of Engineering, Bhubaneshwar, Odisha, India

Abstract

Data analytics play an active role in medical applications to extract relevant information from heaps of data samples. Internet of things (IoT) technology has slowly captured the market and is entering the health care sector too. With the help of big data analytics, various IoT-based devices can auto-monitor the health conditions of patients and can send the status to concerned physicians and family members. Thus, the integration of big data analytics with IoT technology forms a favorable combination in the health care domain. In this chapter, we discuss the two latest trends that include big data analytics and IoT with respect to its relevance in medical fields. We also analyze a health care monitoring system, which is an IoT-based model integrated with big data analytics. The system integrates patient specific information over the cloud. In the more developed model, the implementation was made to monitor the health status of the patients. The developed model was found to be faster and thus it can be easily implemented into a real time patient health monitoring and status management system. Medical experts can take advantage of this system model, thereby providing appropriate information to appropriate patient and doctors at appropriate time.

Keywords

Big data analytics; Cloud; Health care domain; Internet of Things (IoT); Sensors

1. Introduction

2. Sources of health care data

2.1 Electronic health records (EHR)

2.2 Clinical text mining

2.3 Medical imaging data

2.4 Genomic data

2.5 Behavioral data

3. Tools and data analytics interfaces in medical and health care system

3.1 Advanced data visualization (ADV)

3.2 Presto

3.3 Hive

3.4 Vertica

3.5 Key performance indicators (KPI)

3.6 Online analytics processing (OLAP)

3.7 Online transaction processing (OLTP)

3.8 The Hadoop distributed file system (HDFS)

3.9 Casandra file system (CFS)

3.10 Map reduce system

3.11 Complex event processing (CEP)

3.12 Text mining

3.13 Cloud computing

3.14 Mahout

3.15 JAQL

3.16 AVRO

4. Health care with big data challenges

4.1 Issues related to policy and fiscal factors

4.2 Issues related to technology

5. IoT defined

6. IoT for health care

7. Challenges for IoT in health care

8. Evolution of big data in medical IoT

9. Advantages

10. Literature survey

11. Implementation of a real-time big data analytics of IoT-based health care monitoring system

11.1 Components and methods

11.2 Results and discussion

12. Conclusion

1. Introduction

Data analytics acts as a major aspect for application in various fields. Data analytics have emerged as a vital tool for scientists due to the heightened presence of heterogeneous and unstructured data around the world. Scalable data analytics techniques are needed; in medical sectors, massive data is regularly aggregated in several organizations. These data sources act as a resource in deriving insights for enhancing care delivery and minimizing waste. The volume and complex nature of such data is a challenge in analyzing and applying in a real-life health care environment.

2. Sources of health care data

Datasets gathered in health care domains includes quantitative and qualitative data. Quantitative data is of quantifiable nature and used for comparison purpose. Examples include weight, age, temperature, or any other discrete variables. Qualitative data are nonnumerical in nature which is used to represent health related problems. Some examples include male/female or smoker/non-smoker etc. Data sources in medical field include scientific data and clinical data. Clinical data include data related to clinical surveys or epidemiological based information. Scientific data denotes data related to bench sciences. Data recorded and collected in medical domain are of primary and secondary in nature. Primary data refers to the individual person or a group to collect and analyze the data. This collected data may be used for research queries. Secondary data is dependent on the existing data which are already available and is utilized for other purpose. These data are used to answer research-based questions. Fig. 1.1 highlights the health care sources of data samples.

2.1. Electronic health records (EHR)

This is an important source of data in medical field. Electronic health records (EHR) refers to the digital records of patients. Here the data can be accessed from anywhere and whenever required. It may be structured or unstructured. In structured data, all records are properly captured and categorized in a database. But unstructured data records are vague and inconsistent which are presented in the form of static pages of health information. Examples include PDF files, emails, and digital images, audio, or video information. Ultimately these data are transformed into structured records.

Figure 1.1 Health care data sources.

2.2. Clinical text mining

Health care records can be structured, unstructured, or can have text related information. Here text mining can be used to extract useful and sound information from huge raw data. Few text mining methods involve categorization and sentiment analysis. It is used for optimum targeting of drugs, precise disease diagnosis and efficient patient treatment. Natural language processing may be used in health care text mining.

2.3. Medical imaging data

CT scans and X-rays belongs to unstructured data type. Picture Archival & Communication Systems is a system used to store and retrieve clinical imaging data records. In clinical retrieval process, images are deposited in repository of biomedical image data. These image-based data takes huge memory and is complicated in processing.

2.4. Genomic data

These data handles DNA aspects in structural and sequential arrangement of various functionalities of genes. Specific software is required to store and process these data. A repository called genomic database comprises human genomes and association rules related to genomes. This repository determines the identical genetic symptoms influencing health and its associated diseases.

2.5. Behavioral data

The source of behavioral data lies in mobility-based sensor data associated with social network. There exist some social networks which keep track of diseases and their symptoms. Accordingly, they offer better treatment process based on the symptoms involved. Similarly, sensors can be deployed to gather and aggregate disease related data from patients in a health care institutions.

3. Tools and data analytics interfaces in medical and health care system

There exist various tools and applications which are used to determine the progress in clinical data analysis. Some of the widely popular tools used are presented below.

3.1. Advanced data visualization (ADV)

ADV is useful to deal with several types of data. It changes from line chart to standard bars. It is quite easy to use. It offers wide support to analysts in data exploration. It produces very optimum results and used to extract medical hidden patterns in health care data.

3.2. Presto

Presto is a distributed SQL Query engine used in analyzing massive quantity of clinical data. It is applied in a large-scale analysis where data analysis can be done without significant delay.

3.3. Hive

It is also applied to deal with large scale data records. It is not so fast like Presto tool. In fact it performs all Excel sheet tasks effectively. Many industries prefer Hive for medical records storage and retrieval.

3.4. Vertica

This tool is identical to Presto and is utilized in processing huge amount of clinical based data which may be further used for data analytics. It is cost effective and its architecture is simple. It is very scalable in nature. It is advantageous in reducing operational costs, speeding up health care reports and documentation thereby helps in analyzing health patterns of patients.

3.5. Key performance indicators (KPI)

This represents a procedure which makes application of electronic health care records in determining inventions and practices of human beings. Patients who are more vulnerable to hospital environment may be subjected to KPI tool to get better results.

3.6. Online analytics processing (OLAP)

Here the data is organized in multidimensional patterns which perform statistical computation at a great speed. It amplifies data integrity constraints and establishes better quality control. It keeps track of health care records and helps in disease diagnosis.

3.7. Online transaction processing (OLTP)

OLTP and OLAP are interrelated to each other. This tool is useful in processing registration of patients, analyze various operations of patients and result review analysis.

3.8. The Hadoop distributed file system (HDFS)

The performance of clinical data analytics is improved by the use of HDFS which partitions huge data sets into relatively smaller ones. These smaller data samples are distributed across entire system. It removes redundancy of data. It acts as a diagnosis assisting tool and is used to monitor and detect fraud elements and patients symptoms.

3.9. Casandra file system (CFS)

It is very much identical to HDFS. This file system is designed to handle analytical operations and is fault-tolerant.

3.10. Map reduce system

This system deals with massive amount of data. It partitions the chore into subchores and aggregates its output. It efficiently integrates various operational computations into the system. It tracks every server where the chore is being done. The main benefit lies in its higher degree of parallel tasks.

3.11. Complex event processing (CEP)

It is a recent addition in medical sector which helps in monitoring different phases of patient. Complicated event processing is interlinked in real-time analysis.

3.12. Text mining

In medical field text mining systems may act as an advantage in examining medical records from medical centers. It can be useful in devising treatment plans which can develop many protocols. Further treatment of patients can be undertaken relating to such developed guidelines.

3.13. Cloud computing

Cloud computing technology offers higher flexibility in medical field as far as dealing with adaptive variations and health care updates are concerned. It is an addition to clinical sector by reducing the medical costs, enhancing the productivity an optimizing data analysis task.

3.14. Mahout

Mahout is an apache-based project which is intended in developing applications to improve clinical data analytics on Hadoop systems.

3.15. JAQL

JAQL is a procedure-oriented query language useful in processing massive amount of data. Parallel processing of data is feasible by converting higher level queries into lower level ones. It is well suited to work with Map reduce functions.

3.16. AVRO

AVRO is effective in encoding and serialization of data. It enhances data semantics by specifying types of data samples used, semantics and its schema.

4. Health care with big data challenges

The challenges can be categorized into two types:

4.1. Issues related to policy and fiscal factors

In the age of money for service scenario, the medical experts can get paid only when they have a face to face interaction with their patients. It acts as a bottleneck to promote new technologies that encourage interaction without physical presence of patients. Moreover, as we go further away from direct interaction-based models, where there are more financial risks are involved there is more scope of using recent advanced technologies where unnecessary face to face interactions may be avoided. In such cases, face to face interactions with patients are quite expensive while use of advance technologies impacts a positive influence in health outcomes of people.

4.2. Issues related to technology

One of the largest technical obstacle to achieve this mission is the status of medical data. Developed by EHR systems, medical based data records are highly segmented into organization-based silos. Maximum effort is given to deal with this exchange of individual data records in between silos with the use of standard code sets and message structure. But it fails to solve the data fragmentation issue. Recently people in medical arena are visualizing the future generation of medical field lies in data aggregation and not just sharing copies of patient records. The data can be made relevant and useful only when data can be gathered from heterogeneous sources and further normalizing the gathered data and resolving the information with unique identifiers of patients. There are two main benefits of aggregated data.

• It resolves the interoperability issue. Organizations are no more required develop data bridges and convert the data between proprietary systems. They just need to connect data sources to a common API module. This data aggregation forms the basis of effective artificial intelligence technology.

• It provides adequate flexibility thereby allowing artificial intelligence and machine learning to operate efficiently in real-time manner.

5. IoT defined

IoT refers to a computational notion to describe the concept of daily physical objects which are connected to internet such that they are able to identify and distinguish themselves from the rest. This methodology is acutely associated with RFID as the transmission technique. Besides this, it involves sensor and wireless technologies or QR codes. The significance of IoT lies in the fact that the digital representation of object becomes more visible that the object itself. The object is no more interrelated to its user but also is related to its neighboring objects. Some crucial focus areas where IoT analytics can be successfully applied are:

• Forecasting the agriculture production/manufacture

• Machine learning algorithms

• Failure prediction

• Predictive maintenance

• Supply and chain

• Frequent pattern mining.

6. IoT for health care

The main aim of technology in medical field is to connect health care experts with their patients through smart devices. This helps patients to be more aware of their issues and thus the diagnosis becomes effective. It empowers consumer with less inefficiencies and assists doctor in precise decision making. IoT in health domain serves two main purposes which are:

• Enhanced management of disease providing better patient experience.

• Decreased medical costs to make it more affordable for a wider demographic population.

As shown in Fig. 1.2, a survey was conducted by Grand View Research where it is estimated that by the year 2022, IoT in health filed which covers the domain of medical equipment, services, and software is presumed to jump a whopping $300B market expansion. Central agency schemes are also likely to impact and encourage this value for customized smart health care.

Fig. 1.3 highlights the architectural view needed in clinical IoT systems. It consists of three prime components, which include the device layer, which has the body area sensor network embedded into it; Internet-connected smart local access network, which is called a Fog layer; and a Cloud layer for cloud and big data service support. Several applications and firms provide services to various stakeholders within the system by the use of this model. Sensors attached to users are responsible to generate data which is made readily available to medical experts, family members and authorized firms enabling them to verify and validate the issues and diagnosis process at anytime from anywhere as well as assisting health care experts in intelligent decision making. In this new age of information, knowledge extracted from raw data is the need of the hour. This is the age of customers where their associations with health care world are a priority. At this juncture, apps and devices will be applied to develop a health-aware environment. Some of these devices include:

Figure 1.2 Coverage analysis of IoT in health care services.

Figure 1.3 Architectural elements of health care IoT systems [1].

• OpenAPS: closed-loop insulin delivery

• Continuous glucose monitoring (CGM) system

• Activity trackers during cancer treatment

• Connected inhalers

• Ingestible sensors

• Connected contact lenses

• Depression-fighting Apple Watch app

• Coagulation testing

• Arthritis: Apple's ResearchKit

• Parkinson's Project Blue Sky

7. Challenges for IoT in health care

Prime objective of reputed IoT firms is to provide simple and powerful implementations to services of IoT and data handling facility. It helps designers to compose data analytics applications, visualization frameworks and health care IoT apps. Some of the critical capabilities that IoT organizations must be enabled are:

• Simple connectivity: An ideal IoT firm must be competent enough to provide ease of connection to devices thereby facilitating device management functionalities.

• Easy device management: It enables enhanced availability of different assets and resources which lead to improved throughput and reduction in maintenance costs.

• Information ingestion: Intelligent transformation and storage of data is a vital factor In IoT. Information is ingested from distinct sources of data and then relevant information is extracted with the use of data analytics.

• Informative analytics: Proper analysis of raw information is important for optimal decision making and smooth operations. It is used in real-time analytics and monitoring present conditions to respond accordingly. Moreover an intuitive dashboard makes it more simple and effective to understand.

• Reduced risk: Act on warnings and isolate activities collected somewhere in the organizations from a unit console.

8. Evolution of big data in medical IoT

The health care industry is the combination of different sectors. The sectors has the inclusion of medicines, precautionary with the statistical working of the properties and the socialization of care. Health care industries have been included with different nursing home, medical trials, outsourcing, health coverage, telemedicine and other charitable organization. These days the health care industry has been seen from a business point of view. The business provides good profits and other services for the people. Along with this there has been an inclusion of information and communications technology. The ICT cell has been able to provide the health care industry with the provision of different roles for the improvement of health care industry. The system would be able to help the existing system in the exclusion of medical errors. Information related to the system is generated from different sources. Sources for the data includes clinical trials, medicine, exercise, variable symptoms, prescription, laboratory report, insurance data and various other information related to the patient and doctors. The large amount data in the health care industry is growing in an exponential form with current data size in the order of petabytes. This immense growth of data has given rise to various problems related to storage, transfer, and computational analysis. This form of data can be analyzed and processed with the help of traditional relational database system. Moreover, traditional database system can only process structured data. Whereas the data stored in the form of big data is unstructured. With the invention of new and efficient mechanism for the storage and the accessing of the information the ICT would be able to help serve the society in a better storage. The process of implementing of ICT in the health care industry in termed as eHealth. Thus the implementation in the health care industry would help in the processing of data and consecutive analysis and the improvement of the decision-making process for the collection of better treatment solutions for the symptoms for the diseases. One of the top characteristics of the use of health care industry is the richness of data. With the recent development of the diagnostics and the treatment processes, the health care industry has been used to quickly evolve the sector in the previous couple of decades. Several sources are used for generation