1st Edition

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

    395 Pages 133 B/W Illustrations
    by CRC Press

    395 Pages 133 B/W Illustrations
    by CRC Press

    The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications.


    • Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems
    • Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines
    • Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems
    • Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications
    • Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analytics


    This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.

    1. Machine Learning in Healthcare: An Introduction

    Shruti Dambhare and Sanjay Kumar

    2. A Machine Learning Approach to Identify Personality Traits from Social Media

    Arion Mitra, Ankita Biswas, Kanyaka Chakraborty, Ananya Ghosh, Namrata Das, Nikita Ghosh, and Ahona Ghosh

    3. Influence of Content Strategies on Community Engagement over the Healthcare-Related Social Media Pages in India

    Ajitabh Dash

    4. The Impact of Social Media in Fighting Emerging Diseases: A Model-Based Study

    Anal Chatterjee and Suchandra Ganguly

    5. Prediction of Diabetes Mellitus Using Machine Learning

    Salliah Shafi and Gufran Ahmad Ansari

    6. Spectrogram Image Textural Descriptors for Lung Sound Classification

    Bhakti Kaushal, Mukesh D. Patil, Smitha Raveendran, and Gajanan K. Birajdar

    7. Medical Image Analysis Using Machine Learning Techniques: A Systematic Review

    Mustafa A. Al-Asadi and Sakir Tasdemir

    8. Impact of Ensemble-Based Models on Cancer Classification, Its Development, and Challenges

    Barnali Sahu, Sitarashmi Sahu, and Om Prakash Jena

    9. Performance Comparison of Different Machine Learning Techniques towards Prevalence of Cardiovascular Diseases (CVDs)

    Sachin Kamley

    10. Deep Neural Networks in Healthcare Systems

    Biswajit R Bhowmik, Shrinidhi Anil Varna, Adarsh Kumar, and Rahul Kumar

    11. Deep Learning and Multimodal Artificial Neural Network Architectures for Disease Diagnosis and Clinical Applications

    Jeena Thomas and Ebin Deni Raj

    12. A Temporal JSON Model to Represent Big Data in IoT-Based e-Health Systems

    Zouhaier Brahmia, Safa Brahmia, Rafik Bouaziz, and Fabio Grandi

    13. Use of UAVs in the Prevention, Control and Management of Pandemics

    G. Bilotta, V. Barrile, E. Bernardo, and A. Fotia

    14. Implicit Ontology Changes Driven by Evolution of e-Health IoT Sensor Data in the τOWL Semantic Framework

    Zouhaier Brahmia, Abir Zekri, Rafik Bouaziz, and Fabio Grandi

    15. Classification of Text Data in Healthcare Systems – A Comparative Study

    O. Koksal

    16. Predicting Air Quality Index with Machine Learning Models

    G. Abirami, Anindya Das, Navneeth Sreenivasan, and R. Girija


    Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, and Odisha.

    Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at School of Engineering and Technology, Sharda University, Greater Noida, India.

    Dr. Nitin Rakesh is the Head of Computer Science & Engineering Department for B.Tech/M.Tech (CSE/IT), B.Tech CSE-IBM Specializations, B.Tech CSE-I Nurture, BCA/MCA, BSc/MSc-CS at School of Engineering and Technology,at Sharda University, India.

    Dr. Parma Nand is a Dean, School of Engineering Technology, Sharda University Greater Noida.

    Dr. Yousef Farhaoui is a Professor at Moulay Ismail University, Faculty of Sciences and Techniques, Morocco.