Table Of ContentIntelligent Systems Reference Library 233
Janmenjoy Nayak · Asit Kumar Das ·
Bighnaraj Naik · Saroj K. Meher ·
Sheryl Brahnam Editors
Nature-Inspired
Optimization
Methodologies
in Biomedical
and Healthcare
Intelligent Systems Reference Library
Volume 233
Series Editors
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
Lakhmi C. Jain, KES International, Shoreham-by-Sea, UK
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· ·
Janmenjoy Nayak Asit Kumar Das
· ·
Bighnaraj Naik Saroj K. Meher Sheryl Brahnam
Editors
Nature-Inspired Optimization
Methodologies in Biomedical
and Healthcare
Editors
Janmenjoy Nayak Asit Kumar Das
P.G. Department of Computer Science Department of Computer Science
Maharaja Sriram Chandra Bhanja Deo and Technology
(MSCB) University Indian Institute of Engineering Science
Baripada, Odisha, India and Technology
Howrah, West Bengal, India
Bighnaraj Naik
Department of Computer Applications Saroj K. Meher
Veer Surendra Sai University of Technology Systems Science and Informatics Unit
Sambalpur, Odisha, India Indian Statistical Institute (ISI)
Bangalore, India
Sheryl Brahnam
Department of Information Technology
and Cybersecurity
Missouri State University
Springfield, MO, USA
ISSN 1868-4394 ISSN 1868-4408 (electronic)
Intelligent Systems Reference Library
ISBN 978-3-031-17543-5 ISBN 978-3-031-17544-2 (eBook)
https://doi.org/10.1007/978-3-031-17544-2
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Foreword
Today’s healthcare industries yield critical and massive amounts of data from various
sources such as biomedical research, hospital records, clinical records of patients,
clinical examination results, and different health IoT devices. These data require
proper management and analysis to produce meaningful information. Managing and
analyzing this vast data resource with conventional methods is time-consuming
and expensive. Therefore, by providing relevant solutions for improving health-
care services, industries are hunting for lower costs, better outcomes, and value-
based solutions to generate and systematically analyze the vast data resources now
available.
Emerging technologies based on optimization methodologies are vital in handling
this vast amount of data. Having focused my research the last couple of years on new
optimization methods for generating ensembles, I find the chapters in this book,
Nature-Inspired Optimization Methodologies in Biomedical and Healthcare, highly
suggestive. The bulk of the chapters offers many new algorithms and practical tips that
researchers and practitioners should find beneficial. Different types of medical data
are addressed, and various methods for combining nature-inspired optimizations with
v
vi Foreword
deep learning and other neural networks are presented. This volume also provides a
literature review covering the complete history of nature-inspired optimization, an
overview of the state-of-art, and many valuable references.
Prof. Loris Nanni
Dipartimento di Ingegneria
dell’Informazione
University of Padova
Padua, Italy
Preface
Healthcare always provides a wider scope of research among the researchers because
of the drastic increase in healthcare technology from the last few decades. In the
current decade, intelligent healthcare is providing many advances in the diagnosis
and other medical treatments. Generally, healthcare is described as the mechanism of
diagnosing illness and other intellectual and physical downturn to enhance the quality
of human health. Therefore, healthcare systems have been established to reach the
requirements of the indigent people. The healthcare systems require precise informa-
tion and adequately managed health facilities to provide services to the needy people.
Several intelligent healthcare systems are able to provide reliable solution for many
complex medical problems. Proper measurement of results plays an important role
in the healthcare and biomedical fields as it forms the base for medical evaluation,
diagnosis, and prognosis process. In addition, the present healthcare system is also
facing many difficulties such as time delay in diagnosis process, lack of important
information of patient, exploration of voluminous patient data, and so on. There-
fore, proper measurement of results and delivering quality service to patients are
considered as the significant goals of healthcare system. Moreover, the increasing
amount of patient and other medical data with the convergence of related healthcare
domains is leading the medical research into a different direction. The current trends
and techniques give unique opportunity for solving different trivial tasks of medical
and healthcare. But such techniques are on a real need of extracting the convenient
patterns for the characterization of target problems and developing intelligent mech-
anism underlying the noisy and fabricated data, which may ultimately transform such
knowledge into real-life solutions. In recent decades, nature-inspired optimization
algorithms have gained rising popularity in solving complex problems of different
domains because of the knowledge discovery and evolution of natural computing.
The ability to resolve the non-deterministic polynomial problems is considered as the
fundamental cause for the popularity of nature-inspired optimization approaches. As
nature-inspired optimization algorithms offer robust computational tools for complex
optimization problem, numerous algorithms have been developed by the researchers.
Currently, nature-inspired optimization approaches have been utilized in biomedical
vii
viii Preface
and healthcare system to solve the key issues of healthcare sector such as providing
quality service at low cost and to enhance quality of healthcare services.
The problems in the healthcare industry, such as predicting, planning, scheduling,
have not been effectively formulated, and many nature-inspired optimization algo-
rithms are not well known to the healthcare sector. Therefore, the primary purpose of
this book is to provide a detailed perception of the application of nature-inspired opti-
mization algorithms in the healthcare sector to provide effective computer-aided diag-
nosis and prediction for preventing disease at the early stages. With this reasoning,
the proposed book starts with the fundamentals of nature-inspired optimization tech-
niques, implementations, and mechanisms allied with them. It is a pile of various
recently developed nature-inspired optimizations and other amalgamated approaches
utilized for the field of healthcare and bioengineering. Besides, the book is an ample
collection of the latest and advanced intelligent methods integrated with nature-
inspired optimization approaches. It will head for the challenges encountered by
the hospitals and healthcare organizations for practical investigation, depository, and
analysis of data. Further, the book will assist in focusing on future research chal-
lenges and guide the practitioners and researchers. Also, a particular prominence will
be on the accumulation of applicable advanced mechanisms, automated tools, and
possible approaches for resolving sophisticated biomedical problems to data scien-
tists, experts, and research scholars in a broader sense. In addition, many researchers
and students would benefit from this book as it provides in-depth knowledge of the
theory and application of nature-inspired optimization approaches and allied hybrid
technologies in the healthcare sector. Moreover, researchers can also have the scope
to find a lot of unexplored areas to carry out novel research in the future. This volume
comprises 12 chapters and is organized as follows:
Chapter 1 provides a brief description of the most significant nature-inspired
optimization algorithms that have been developed from the past to the present and
their role in solving computationally complex problems in real-life applications of
various domains. In addition, Mohammed Aarif K. O. et al. have also provided an
overview of the performance of different nature-inspired optimization algorithms
along with their challenges and further research directions.
In Chap. 2, an inferring classic model has been proposed by R. Jayashree for
the efficient prediction of spreading patterns of coronavirus infection using swarm
learning approach known as the Recursive Particle Swarm Optimization algorithm.
In the proposed model, author makes use of recursive particle swarm optimization
algorithm for finding the optimal hyperparameters. Moreover, the recursive particle
swarm optimization algorithm automatically updates the features whenever infected
person transfers from the state of symptomatic to recovery/death or asymptomatic to
symptomatic/recovery because of its dynamic nature. The suggested model has been
evaluated using data provided by the official coronavirus website, and the results
indicate that the suggested model attains better accuracy in predicting patterns of
coronavirus infection in comparison to the Bayesian Monte Carlo version.
Chapter 3 is about developing an optimized ensemble learning machine-based
framework for the accurate prediction of obesity levels from physical conditions and
eating habits. The system developed by Geetanjali Bhoi et al. makes use of gradient
Preface ix
boosting decision tree approach for handling noisy and high variance data. Moreover,
the suggested approach also makes use of artificial particle optimization approach for
finding optimal hyperparameters of the model. The authors have identified 16 distinct
factors relevant to obesity and determined age, gender, height, weight, and family
history with overweight as the most significant factors of predicting obesity levels.
The developed system has been evaluated, and results indicate that the developed
system efficiently predicts distinct obesity level of patients when compared with
other similar approaches.
In Chap. 4, Natalia Obukhova et al. suggested an approach for color correction
with minimum error using the perceptual metric CIEDE2000. In this approach, a
separate target function is used to represent each color of the palette. The approach
also makes use of third-order polynomial with 11 coefficients to represent each color
channel as the algorithm matches with the palette. Basically, the algorithm used to
estimate transformation function coefficients based on multi-objective optimization
involves three steps. In the first step, the starting point is determined by the least square
method. Next, Broyden–Fletcher–Goldfarb–Shanno algorithm is used for performing
line search. Finally, in the last step, Nelder-Mead Algorithm has been used in the
solution refinement. The developed approach has been validated, and results indicate
that for all colors from palette, error rate is less than 1. After matching, if the error
is more than 1 then the difference between colors will be visible to observer.
Chapter 5 developed a data-driven heart failure detection model using extreme
gradient boosting an ensemble learning technique. The system developed by Etuari
Oram et al. uses of dataset consisting of clinical and lifestyle information of 299
heart failure patients, including 105 women and 194 men. 13 features are captured
to represent the clinical and lifestyle information of heart failure patients. Then the
optimal parameters of extreme gradient boosting ensemble learning technique such
as subsample, L2 regularization, L1 regularization, learning rate, max depth, and max
delta step have been determined using Gravitational search algorithm. Finally, the
model has been evaluated, and empirical results indicate developed system obtains
better performance when compared with other similar approaches.
Chapter 6 introduces an approach that combines the capability of various nature-
inspired optimization algorithms along with a learning model of artificial neural
networks for generating more rationalized and precise output of the neural network.
Initially, the functionality of various nature-inspired optimization algorithms, such
as genetic algorithm, particle swarm optimization algorithm, differential evolution,
colony optimization algorithm, bat algorithm and black hole algorithm along with
their advantages and disadvantages, has been explained by Soumen Kumar Pati et al.
Then, the nature-inspired optimization algorithms have been integrated with artificial
neural networks to solve complex optimization problems. From the experimental
results, it is concluded that hybrid models have produced better performance in
terms of accuracy, precision, and convergence on global optima.
Chapter 7 is about extracting relevant information from medical reports
using fuzzy theory and nature-inspired optimization algorithms. Initially, sentence
tokenization has been applied by Chirantana Mallick and Asit Kumar Das to pre-
process the extracted data. Then, BioBERT model has been applied to perform
