Health Information Science Amit Kumar Manocha Shruti Jain Mandeep Singh Sudip Paul  Editors Computational Intelligence in Healthcare Health Information Science Series Editor Yanchun Zhang, Victoria University, Melbourne, VIC, Australia Editorial Board Riccardo Bellazzi, University of Pavia, PAVIA, Pavia, Italy Leonard Goldschmidt, Stanford University Medical School, STANFORD, CA, USA Frank Hsu, Fordham University, Bronx, NY, USA Guangyan Huang, Centre for Applied Informatics, Victoria University, Melbourne, Australia Frank Klawonn, Helmholtz Centre for Infection Research, Braunschweig, Germany Jiming Liu , Hong Kong Baptist University, Kowloon, Hong Kong Zhijun Liu, Hebei University of Engineering, Hebei Sheng, China Gang Luo, University of Utah, Yorktown Heights, USA Jianhua Ma, Hosei University, Tokyo, Japan Vincent Tseng, National Cheng Kung University, Tainan City, Taiwan Dana Zhang, Google, Mountain View, USA Fengfeng Zhou, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, Shenzhen, China With the development of database systems and networking technologies, Hospital Information Management Systems (HIMS) and web-based clinical or medical systems (such as the Medical Director, a generic GP clinical system) are widely used in health and clinical practices. Healthcare and medical service are more data- intensive and evidence-based since electronic health records are now used to track individuals’ and communities’ health information. These highlights substantially motivate and advance the emergence and the progress of health informatics research and practice. Health Informatics continues to gain interest from both academia and health industries. The significant initiatives of using information, knowledge and communication technologies in health industries ensures patient safety, improve population health and facilitate the delivery of government healthcare services. Books in the series will reflect technology’s cross-disciplinary research in IT and health/medical science to assist in disease diagnoses, treatment, prediction and monitoring through the modeling, design, development, visualization, integration and management of health related information. These technologies include information systems, web technologies, data mining, image processing, user interaction and interfaces, sensors and wireless networking, and are applicable to a wide range of health-related information such as medical data, biomedical data, bioinformatics data, and public health data. Series Editor:, Yanchun Zhang, Victoria University, Australia;  Editorial Board:  Riccardo Bellazzi, University of Pavia, Italy;  Leonard Goldschmidt, Stanford University Medical School, USA; Frank Hsu, Fordham University, USA; Guangyan Huang, Victoria University, Australia; Frank Klawonn, Helmholtz Centre for Infection Research, Germany; Jiming Liu, Hong Kong Baptist University, Hong Kong, China; Zhijun Liu, Hebei University of Engineering, China;  Gang Luo, University of Utah, USA;  Jianhua Ma, Hosei University, Japan; Vincent Tseng, National Cheng Kung University, Taiwan; Dana Zhang, Google, USA; Fengfeng Zhou, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China More information about this series at http://www.springer.com/series/11944 Amit Kumar Manocha • Shruti Jain Mandeep Singh • Sudip Paul Editors Computational Intelligence in Healthcare Editors Amit Kumar Manocha Shruti Jain Electrical Engineering Electronics & Communication Engineering MRS Punjab Technical University Jaypee University of Information Technol Bathinda, India Solan, India Mandeep Singh Sudip Paul Electrical & Instrumentation Biomedical Engineering Department Engineering North Eastern Hill University Thapar University Shillong, India Patiala, India Series Editor Yanchun Zhang ISSN 2366-0988 ISSN 2366-0996 (electronic) Health Information Science ISBN 978-3-030-68722-9 ISBN 978-3-030-68723-6 (eBook) https://doi.org/10.1007/978-3-030-68723-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Preface Kirlian photography is a technique based on electrical coronal discharge. In this technique, the photographic plate made of metal is charged with a high-voltage source. The process is also quite simple, and it is recommended to use transparent electrodes instead of the discharge plate. It is an exciting method to capture the corona discharge of individual subjects. Medical image compression finds extensive applications in healthcare, teleradiology, teleconsultation, telemedicine, and telematics. Efficient compression algorithms implement the development of picture achieving and communication systems (PACS). To reduce transmission time and storage costs, efficient image compression methods without degradation of images are needed. In medical image compression techniques, the lossy and lossless meth- ods do not produce optimum compression with no information loss. The circuit complexity and propagation delay are the primary concern in the design of the digital circuit. In the binary system, as the number of bits increases, the computation speed is limited by carrying. Consequently, it offers low storage density, enormous complexity, and O (n) carry, producing a delay in n-bit base on two application problems. For less complexity and higher information storage den- sity, higher radix number system can be used. Quaternary signed digit (QSD) num- ber system performs carry-free addition and borrows free subtraction. The concept of telemedicine and telehealth is still a novice one to most practitioners. However, the continued advances in technology can demand its usability from a new genera- tion of tech-savvy people due to its convenience, cost-effectiveness, and intelligent features. These include store and forward techniques, real-time interactive modes, remote monitoring, and smartphones for healthcare services like m-health. In the era of digitization, tracking information on a real-time basis is one of the eminent tasks. Wearable technology involves electronics incorporated into items that can be comfortably worn on a body mainly used to detect, analyze, and transmit contemporaneous information. Wearable technology has applications in many fields, such as health and medicine, fitness, education, gaming, finance, music, and transportation. Wearable devices have become quite inevitable as technology in the medical electronics field advances. These devices are highly cost-effective and por- table, making it easy to use, efficient, and safe. v vi Preface Because people desire a high quality of life, health is a vital standard of living that attracts considerable attention. Thus, the development of methods that enable rapid and real-time evaluation and monitoring of the social health status has been crucial. Moreover, to attain an intuitive insight to detect, monitor, identify, and accu- racy is an essential parameter. In this, concerning the characteristics of time-series data acquired using health condition monitoring through sensors, recommendations and bits of advice are provided to apply deep learning (DL) methods to human body evaluation in specific fields. Emotion detection has been carried out through various techniques such as EEG and image and video recording of facial expressions, body gestures, text-based emotion identification, etc. The non-contact temperature sensor used to detect breathing patterns for various emotions is considered for this to know emotional activities like happiness, surprise, sadness, and anger. Twelve features are taken from the wavelet transformed signal features to get emotional status. E-health means the availability of various health facilities electronically without going anywhere. There is a tremendous demand for implementing such techniques in rural areas because of the spread of the COVID-19 pandemic. Patients and dis- abled people from villages faced many difficulties due to the closure of transporta- tion facilities during the lockdown period. There is a significant requirement to develop a connected network that consists of various medical devices like E-health systems to fulfill the needs of those who live in rural areas. Osteoporosis is a common disease prevalent mostly among elderly individuals. The characteristics of osteoporosis include increased fragility, which is caused due to the reduction of the bone's absorption capability. This leads to an increase in the porosity, reduction in the bone's elastic stiffness, and thinning of the cortical wall. Osteoporosis increases the risk of fractures and hence can cause suffering and also leads to economic loss. The current standard method of detecting osteoporosis is dual-energy X-ray absorptiometry (DXA), which cannot predict whether a person is suffering from porous bones. Infusion is a mechanism by which an infusion system is used to administer fluids or medications via the intravenous, subcutaneous, epidural, or internal path to the patient in solution. For the healthcare drug delivery devices, exact dosing is crucial. The use of smart pumps can avoid errors resulting from an incorrect dose, dose rate, or solution concentration resulting from the ordering provider, as well as errors resulting from human failures in the programming of pumps. Cardiovascular diseases(CVDs)are significant reasons for mortality in the world population, and the number of cases is surging every year. Due to coronary artery disease (CAD) and congestive heart failure(CHF), the mortality rate is higher than any other CVD type. Therefore, early detection and diagnosis of CAD and CHF patients are essential. An automated non-invasive approach has been used to detect CAD and CHF patients using attributes extracted from heart rate variability (HRV) signal. Electrocardiogram (ECG) signal carries the most critical information in cardiol- ogy. Care should be taken to avoid interference in the ECG. To avoid too many computations, IIR digital filters have fewer coefficients and the potential of sharp Preface vii roll-offs, which is acceptable for real-time processing. Coefficients of IIR digital filters are worked out with the genetic algorithm (GA). Using a designed filter, the ECG signal was de-noised by removing the interference. Results indicate that there is noise reduction in the ECG signal. Heart rate (HR) is one of the essential physiological parameters and acts as an indicator of a person's physiological condition. The algorithm for face detection is used to recognize human faces, and HR information is extracted from the color variation in facial skin caused by blood circulation. The variation in the blood cir- culation causes changes in the pixel intensity of the live video recorded. The extrac- tion of the specific frequency is achieved by bandpass filtering, and the pixel intensity average is calculated. Finally, the HR of the subject is measured using the frequency-domain method. Melanoma is the deadlier type of skin cancer. Proper diagnosis and a perfect optimized segmentation methodology rely on thresholding, and watershed algo- rithms are implemented on dermoscopy images, and the differentiated object is derived for GLCM attributes and qualitative techniques. The attributes are supplied into the support vector machine as a source to identify malignant or benign samples. As perceived by experimental findings, the SVM classification system with water- shed segmentation can distinguish benign and malignant melanoma with a higher overall categorization efficiency. Gastroenterology is a field of medicine that has its roots deeply embedded in the technological revolution of the twenty-first century. It emerges as a specialty domi- nated by ever-evolving imaging and diagnostic modalities that have surpassed archaic means of diagnosing and treating various gastroenterological disorders. Advancement in cloud computing technology can change healthcare professionals in gastrointestinal (GI) work in the inpatient and outpatient settings. A coronavirus is a group of infectious diseases that are caused by similar viruses called coronaviruses. The disease's seriousness can vary from mild to lethal in human beings, causing severe illness in older adults and those with health issues like cardiovascular disease, diabetes, chronic respiratory, and cancer. There are a limited number of test kits available in the hospitals as the number of positive cases increases daily. Hence, an automated COVID-19 detection system must be imple- mented as an alternative diagnostic method to pause COVID-19 spread in the population. Around 2.1 million women have breast cancer per year globally. The prevalence of breast cancer ranges across nations, but in most instances, the second cause of death for the female population is this form of cancer. Mammography research, which shapes the expert's auto-exploration and manual exploration, is the most powerful technology for the early diagnosis of breast cancer. In the detection of calcifications and masses in the mammography photograph, there is a considerable research effort, apart from other potential anomalies, since these two forms of arti- facts are the best markers of a possible early stage of breast cancer. Artificial intelligence (AI) has solved some of the complex problems virtually in our day-to-day life in today's world. COVID-19, social distancing, and sanitization are new norms of life. The primary diagnostic tool is radio imaging; however, it is viii Preface much easier to diagnose the disease with artificial intelligence (AI) based deep learning methods. Computer vision is a scientific field that deals with how comput- ers can be made to understand the real world from digital images or videos. Emerging advances in bioinformatics enable home computers to become power- ful supercomputers, reduce research expenses, enhance scientific efficiency, and accelerate novel discoveries. Bioinformatics can be understood as a field of data science based on computers and biology's amalgamation. Hence, to understand the complexity of underlying diseases and its mechanism, bioinformatics can be a fun- damental approach to understanding bioinformatics, various omics tools, applica- tions, health informatics, and the healthcare system. The exponential growth and easy availability of biological and healthcare data have offered a movement in healthcare data science research activity. The tradi- tional methods are incapable of processing and managing enormous quantities of complex, high-dimensional healthcare data in volume and variety. Recently, data science technologies have been increasingly used in the research of biomedical and healthcare informatics. Bathinda, India Amit Kumar Manocha Solan, India Shruti Jain Patiala, India Mandeep Singh Shillong, India Sudip Paul Acknowledgment We want to extend our gratitude to all the chapter authors for their sincere and timely support to make this book a grand success. We are equally thankful to all members of Springer Nature’s executive board for their kind approval and appointing us as editors of this book. We want to extend our sincere thanks to Susan Evans, Henry Rodgers, Shanthini Kamaraj, and Kamiya Khatter at Springer Nature for their valuable suggestions and encouragement throughout the project. It is with immense pleasure that we express our thankfulness to our colleagues for their support, love, and motivation in all our efforts during this project. We are grateful to all the reviewers for their timely review and consent, which helped us improve the quality of the book. We may have inadvertently left out many others, and we sincerely thank all of them for their help. Amit Kumar Manocha Shruti Jain Mandeep Singh Sudip Paul ix