, Bio-inspired Algorithms in
Machine Learning and
Deep Learning for Disease Detection
Editors:
Balasubramaniam S
School of Computer Science and Engineering
Kerala University of Digital Sciences, Innovation and
Technology (Formerly IIITM-K), Digital University Kerala
Thiruvananthapuram, Kerala, India
Seifedine Kadry
Department of Applied Data Science
Noroff University College, Kristiansand, Norway
or
Department of Computer Science and Mathematics
Lebanese American University, Beirut, Lebanon
Manoj Kumar T K
School of Digital Sciences
Kerala University of Digital Sciences, Innovation and Technology
Thiruvananthapuram, Kerala, India
K. Satheesh Kumar
School of Digital Sciences
Kerala University of Digital Sciences, Innovation and Technology
Thiruvananthapuram, Kerala, India
,First edition published 2025
by CRC Press
2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2025 Balasubramaniam S, Seifedine Kadry, Manoj Kumar T K and K. Satheesh Kumar
CRC Press is an imprint of Taylor & Francis Group, LLC
Reasonable efforts have been made to publish reliable data and information, but the
author and publisher cannot assume responsibility for the validity of all materials or
the consequences of their use. The authors and publishers have attempted to trace
the copyright holders of all material reproduced in this publication and apologize to
copyright holders if permission to publish in this form has not been obtained. If any
copyright material has not been acknowledged please write and let us know so we may
rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted,
reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other
means, now known or hereafter invented, including photocopying, microfilming, and
recording, or in any information storage or retrieval system, without written permission
from the publishers.
For permission to photocopy or use material electronically from this work, access www.
copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood
Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC
please contact
Trademark notice: Product or corporate names may be trademarks or registered
trademarks and are used only for identification and explanation without intent to infringe.
Library of Congress Cataloging-in-Publication Data (applied for)
ISBN: 978-1-032-86548-5 (hbk)
ISBN: 978-1-032-88509-4 (pbk)
ISBN: 978-1-003-53815-8 (ebk)
DOI: 10.1201/9781003538158
Typeset in Times New Roman
by Prime Publishing Services
, Preface
Currently, computational intelligence approaches are utilised in various science
and engineering applications to analyse information, make decisions, and achieve
optimisation goals. Over the past few decades, various techniques and algorithms
have been created in disciplines such as genetic algorithms, artificial neural
networks, evolutionary algorithms, and fuzzy algorithms. In the coming years,
intelligent optimisation algorithms are anticipated to become more efficient in
addressing various issues in engineering, scientific, medical, space, and artificial
satellite fields, particularly in early disease diagnosis. A metaheuristic in computer
science is designed to discover optimisation algorithms capable of solving intricate
issues. Metaheuristics are optimisation algorithms that mimic biological behaviours
of animals or birds and are utilised to discover the best solution for a certain
problem. A meta-heuristic is an advanced approach used by heuristics to tackle
intricate optimisation problems. A metaheuristic in mathematical programming
is a method that seeks a solution to an optimisation problem. Metaheuristics
utilise a heuristic function to assist in the search process. Heuristic search can be
categorised as a blind or informed search. Metaheuristic optimisation algorithms
are gaining popularity in various applications due to their simplicity, independence
from data trends, ability to find optimal solutions, and versatility across different
fields.
Recently, many nature-inspired computation algorithms have been utilised
to diagnose people with different diseases. Nature-inspired methodologies are
now widely utilised across several fields for tasks such as data analysis, decision-
making, and optimisation. Techniques inspired by nature are categorised as either
biology-based or natural phenomena-based. Bio-inspired computing encompasses
various topics in computer science, mathematics, and biology in recent years.
Bio-inspired computer optimisation algorithms are a developing method that
utilises concepts and inspiration from biological development to create new
and resilient competitive strategies. Bio-inspired optimisation algorithms have
gained recognition in machine learning and deep learning for solving complicated
issues in science and engineering. Utilising BIAs learning methods with machine
learning and deep learning shows great promise for accurately classifying medical
conditions.
This book explores the potential benefits of bio-inspired algorithms (BIAs)
and their application in machine learning and deep learning models for disease
diagnosis, including COVID-19, heart diseases, cancer, diabetes, and some other
diseases. It discusses the advantages of using bio-inspired algorithms in disease
diagnosis and concludes with research directions and future prospects in this field.
Machine Learning and
Deep Learning for Disease Detection
Editors:
Balasubramaniam S
School of Computer Science and Engineering
Kerala University of Digital Sciences, Innovation and
Technology (Formerly IIITM-K), Digital University Kerala
Thiruvananthapuram, Kerala, India
Seifedine Kadry
Department of Applied Data Science
Noroff University College, Kristiansand, Norway
or
Department of Computer Science and Mathematics
Lebanese American University, Beirut, Lebanon
Manoj Kumar T K
School of Digital Sciences
Kerala University of Digital Sciences, Innovation and Technology
Thiruvananthapuram, Kerala, India
K. Satheesh Kumar
School of Digital Sciences
Kerala University of Digital Sciences, Innovation and Technology
Thiruvananthapuram, Kerala, India
,First edition published 2025
by CRC Press
2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431
and by CRC Press
4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2025 Balasubramaniam S, Seifedine Kadry, Manoj Kumar T K and K. Satheesh Kumar
CRC Press is an imprint of Taylor & Francis Group, LLC
Reasonable efforts have been made to publish reliable data and information, but the
author and publisher cannot assume responsibility for the validity of all materials or
the consequences of their use. The authors and publishers have attempted to trace
the copyright holders of all material reproduced in this publication and apologize to
copyright holders if permission to publish in this form has not been obtained. If any
copyright material has not been acknowledged please write and let us know so we may
rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted,
reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other
means, now known or hereafter invented, including photocopying, microfilming, and
recording, or in any information storage or retrieval system, without written permission
from the publishers.
For permission to photocopy or use material electronically from this work, access www.
copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood
Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC
please contact
Trademark notice: Product or corporate names may be trademarks or registered
trademarks and are used only for identification and explanation without intent to infringe.
Library of Congress Cataloging-in-Publication Data (applied for)
ISBN: 978-1-032-86548-5 (hbk)
ISBN: 978-1-032-88509-4 (pbk)
ISBN: 978-1-003-53815-8 (ebk)
DOI: 10.1201/9781003538158
Typeset in Times New Roman
by Prime Publishing Services
, Preface
Currently, computational intelligence approaches are utilised in various science
and engineering applications to analyse information, make decisions, and achieve
optimisation goals. Over the past few decades, various techniques and algorithms
have been created in disciplines such as genetic algorithms, artificial neural
networks, evolutionary algorithms, and fuzzy algorithms. In the coming years,
intelligent optimisation algorithms are anticipated to become more efficient in
addressing various issues in engineering, scientific, medical, space, and artificial
satellite fields, particularly in early disease diagnosis. A metaheuristic in computer
science is designed to discover optimisation algorithms capable of solving intricate
issues. Metaheuristics are optimisation algorithms that mimic biological behaviours
of animals or birds and are utilised to discover the best solution for a certain
problem. A meta-heuristic is an advanced approach used by heuristics to tackle
intricate optimisation problems. A metaheuristic in mathematical programming
is a method that seeks a solution to an optimisation problem. Metaheuristics
utilise a heuristic function to assist in the search process. Heuristic search can be
categorised as a blind or informed search. Metaheuristic optimisation algorithms
are gaining popularity in various applications due to their simplicity, independence
from data trends, ability to find optimal solutions, and versatility across different
fields.
Recently, many nature-inspired computation algorithms have been utilised
to diagnose people with different diseases. Nature-inspired methodologies are
now widely utilised across several fields for tasks such as data analysis, decision-
making, and optimisation. Techniques inspired by nature are categorised as either
biology-based or natural phenomena-based. Bio-inspired computing encompasses
various topics in computer science, mathematics, and biology in recent years.
Bio-inspired computer optimisation algorithms are a developing method that
utilises concepts and inspiration from biological development to create new
and resilient competitive strategies. Bio-inspired optimisation algorithms have
gained recognition in machine learning and deep learning for solving complicated
issues in science and engineering. Utilising BIAs learning methods with machine
learning and deep learning shows great promise for accurately classifying medical
conditions.
This book explores the potential benefits of bio-inspired algorithms (BIAs)
and their application in machine learning and deep learning models for disease
diagnosis, including COVID-19, heart diseases, cancer, diabetes, and some other
diseases. It discusses the advantages of using bio-inspired algorithms in disease
diagnosis and concludes with research directions and future prospects in this field.
