Medical Big Data and Internet of Medical Things

Advances, Challenges and Applications

Aboul Hassanien, Nilanjan Dey, Surekha Borra, Aboul Ella Hassanien, Nilanjan Dey, Surekha Borra

• Publisher: CRC Press
• Year: 2018
• ISBN:
  9781351030366
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

Big data and the Internet of Things (IoT) play a vital role in prediction systems used in biological and medical applications, particularly for resolving issues related to disease biology at different scales. Modelling and integrating medical big data with the IoT helps in building effective prediction systems for automatic recommendations of diagnosis and treatment. The ability to mine, process, analyse, characterize, classify and cluster a variety and wide volume of medical data is a challenging task. There is a great demand for the design and development of methods dealing with capturing and automatically analysing medical data from imaging systems and IoT sensors. Addressing analytical and legal issues, and research on integration of big data analytics with respect to clinical practice and clinical utility, architectures and clustering techniques for IoT data processing, effective frameworks for removal of misclassified instances, practicality of big data analytics, methodological and technical issues, potential of Hadoop in managing healthcare data is the need of the hour. This book integrates different aspects used in the field of healthcare such as big data, IoT, soft computing, machine learning, augmented reality, organs on chip, personalized drugs, implantable electronics, integration of bio-interfaces, and wearable sensors, devices, practical body area network (BAN) and architectures of web systems.

Key Features:

• 
  
  

• Addresses various applications of Medical Big Data and Internet of Medical Things in real time environment

• 
  
  

• Highlights recent innovations, designs, developments and topics of interest in machine learning techniques for classification of medical data

• 
  
  

• Provides background and solutions to existing challenges in Medical Big Data and Internet of Medical Things

• 
  
  

• Provides optimization techniques and programming models to parallelize the computationally intensive tasks in data mining of medical data

• 
  
  

• Discusses interactions, advantages, limitations, challenges and future perspectives of IoT based remote healthcare monitoring systems.

• 
  
  

• Includes data privacy and security analysis of cryptography methods for the Web of Medical Things (WoMT)

• 
  
  

• Presents case studies on the next generation medical chair, electronic nose and pill cam are also presented.

1

Big Data Mining Methods in Medical Applications

Laura Elezabeth and Ved P. Mishra

Contents

Chapter and Scope of the Book

1.1Introduction

1.2Rise of Big Data

1.2.1Growth Rate

1.2.2Benefits of Big Data

1.2.3Big Data Analytics Usage Across Industries

1.3Medical Data as Big Data

1.3.1Benefits of Medical Big Data

1.4Big Data Mining Process

1.4.1Data Mining Techniques

1.4.1.1Comparative Analysis of Data Mining Techniques

1.4.2Data Mining Process

1.4.3Data Mining Tools

1.5Data Mining in Medical Data

1.5.1Application of Big Data Mining in Medical Field

1.6Challenges Faced in Application of Big Data in the Medical Field

1.7Future of Medical Big Data Application

1.8Data Mining Tool: Hadoop

1.9Conclusion

References

Chapter and Scope of the Book

The book Medical Big Data and Internet of Medical Things: Advances, Challenges, and Applications addresses advances in mining, learning, and investigation of huge volume of medical data coming about at a high rate from both ongoing technologies and disconnected frameworks. This book presents scientific categorizations, patterns and issues. For example, veracity in distributive, dynamic, various information gathering, information administration, information models, theories testing, preparing, approval, demonstrate building, improvement procedures and administration of therapeutic huge information gathered from different, heterogeneous IoT devises, systems, stages and technologies.

This chapter discusses data related to human health and medicine and how it can be stored, searched, shared, analysed and presented in ingenious ways. In this chapter, we will see how data mining technology is more convenient for integrating this medical data for a variety of applications. This chapter aims at analysing the existing data mining methodologies, resolving the existing drawbacks, investigating its future potential and proposing a multi-relational and accumulative employment for mining data in a multi-relational format.

1.1Introduction

Big data is a term describing structured and unstructured data sets of large volume, which are evaluated computationally to reveal progressions, associations and patterns specifically relating to human interactions and behaviour. To look for significant or germane information from large data sets, data mining is used, which is the method of digging through the data, involving relatively intricate search operations that return definite and explicitly targeted results.

In the medical field, huge amounts of data are generated, from the patient’s personal information to medical histories, and genetic data to clinical data. This medical big data is stored not simply for the sake of storing, but contains valuable information, which if and when analysed and methodized properly, can aid in understanding the ‘concepts’ of illness and health and thus bring about major breakthroughs in the medical field, especially in the areas of disease diagnosis and prevention.

With the aid of computers and technology, this medical data can be analysed faster in a less cumbersome manner. We can thus draw meaningful and reliable conclusions regarding the health of a person. Data mining technology has opened a new door to disease diagnosis. Similarly, in order to provide effective treatment for a disease’s triennial prevention, data mining can be used. If the assumptions are true and the results are reliable, this could be the beginning of the absolute prevention or even eradication of diseases. Medical data can also be used for healthcare and drug administrative purposes. Big data mining can aid in analysing medical operation indicators of hospitals to help hospital administrators provide data support for medical decision-making.

Dealing with huge amounts of data has many issues related to data integrity, security and inconsistency. Data mining methods and their applications in the medical field is a new concept, although data mining methods have been applied in other fields for quite a while. Therefore, we face issues of practicality. Also, if the medical assumptions deduced from the data are wrong, all this work would be futile. Therefore, in this case, science and technology must go hand in hand.

This chapter aims at analysing the existing data mining methodologies, resolving the existing drawbacks, investigating future potentials and proposing a multi-relational and accumulative employment for mining data in a multi-rel...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• Editors
• Contributors
• Chapter 1: Big Data Mining Methods in Medical Applications
• Chapter 2: Approaches in Healthcare Using Big Data and Soft Computing
• Chapter 3: Implantable Electronics: Integration of Bio-Interfaces, Devices and Sensors
• Chapter 4: Challenges in Designing Software Architectures for Web-Based Biomedical Signal Analysis
• Chapter 5: Handling of Medical Imbalanced Big Data Sets for Improved Classification Using Adjacent_Extreme Mix Neighbours Oversampling Technique (AEMNOST)
• Chapter 6: A Big Data Framework for Removing Misclassified Instances Based on Fuzzy Rough
• Chapter 7: Fuzzy C-Mean and Density-Based Spatial Clustering for Internet of Things Data Processing
• Chapter 8: Parallel Data Mining Techniques for Breast Cancer Prediction
• Chapter 9: A MapReduce Approach to Analysing Healthcare Big Data
• Chapter 10: IoT and Robotics in Healthcare
• Chapter 11: Internet of Medical Things: Remote Healthcare and Health Monitoring Perspective
• Chapter 12: A Comparative Analysis of Classical Cryptography versus Quantum Cryptography for Web of Medical Things (WoMT)
• Index