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Nature-Inspired Optimization Methodologies in Biomedical and Healthcare

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  • © 2023

Overview

Editors:
  1. Janmenjoy Nayak

    1. P.G. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCB) University, Baripada, India

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  2. Asit Kumar Das

    1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, India

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  3. Bighnaraj Naik

    1. Department of Computer Applications, Veer Surendra Sai University of Technology, Sambalpur, India

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  4. Saroj K. Meher

    1. Systems Science and Informatics Unit, Indian Statistical Institute(ISI), Bangalore, India

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  5. Sheryl Brahnam

    1. Department of Information Technology and Cybersecurity, Missouri State University, Springfield, USA

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  • Presents recent research on nature-inspired optimization methodologies in biomedical and health care
  • Covers advanced methodologies, challenges, and solutions to diversified healthcare issues
  • Presents applicable advanced mechanisms, automated tools, and possible approaches for solving biomedical problems

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 233)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Nature-Inspired Optimization Algorithms: Past to Present

    • K. O. Mohammed Aarif, P. Sivakumar, Mohamed Yousuff Caffiyar, B. A. Mohammed Hashim, C. Mohamed Hashim, C. Abdul Rahman
    Pages 1-32

  3. Preventing the Early Spread of Infectious Diseases Using Particle Swarm Optimization

    • R. Jayashree
    Pages 33-47

  4. Optimized Gradient Boosting Tree-Based Model for Obesity Level Prediction from patient’s Physical Condition and Eating Habits

    • Geetanjali Bhoi, Etuari Oram, Bighnaraj Naik, Danilo Pelusi
    Pages 49-69

  5. Multi-Objective Optimization Algorithms in Medical Image Analysis

    • Natalia Obukhova, Alexandr Motyko, Alexandr A. Pozdeev
    Pages 71-95

  6. Heart Failure Detection from Clinical and Lifestyle Information using Optimized XGBoost with Gravitational Search Algorithm

    • Etuari Oram, Bighnaraj Naik, Geetanjali Bhoi, Danilo Pelusi
    Pages 97-112

  7. NIANN: Integration of ANN with Nature-Inspired Optimization Algorithms

    • Soumen Kumar Pati, Ayan Banerjee, Manan Kumar Gupta, Rinita Shai
    Pages 113-145

  8. Hybridization of Fuzzy Theory and Nature-Inspired Optimization for Medical Report Summarization

    • Chirantana Mallick, Asit Kumar Das
    Pages 147-174

  9. An Optimistic Bayesian Optimization Based Extreme Learning Machine for Polycystic Ovary Syndrome Diagnosis

    • H. Swapnarekha, Pandit Byomakesha Dash, Janmenjoy Nayak, Ashanta Ranjan Routray
    Pages 175-193

  10. Diabetes Twitter Classification Using Hybrid GSA

    • V. Diviya Prabha, R. Rathipriya
    Pages 195-219

  11. Advance Machine Learning and Nature-Inspired Optimization in Heart Failure Clinical Records Dataset

    • Dukka Karun Kumar Reddy, H. S. Behera, Weiping Ding
    Pages 221-246

  12. Early Detection of Chronic Obstructive Pulmonary Disease Using LSTM-Firefly Based Deep Learning Model

    • P. Suresh Kumar, Pandit Byomakesha Dash, B. Kameswara Rao, S. Vimal, Khan Muhammad
    Pages 247-267

  13. GACO: A Genetic Algorithm with Ant Colony Optimization—Based Feature Selection for Breast Cancer Diagnosis

    • Satyajit Panigrahi, H. Swapnarekha, Sharmila Subudhi
    Pages 269-293
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Keywords

About this book

This book introduces a variety of well-proven and newly developed nature-inspired optimization algorithms solving a wide range of real-life biomedical and healthcare problems. Few solo and hybrid approaches are demonstrated in a lucid manner for the effective integration and finding solution for a large-scale complex healthcare problem. In the present bigdata-based computing scenario, nature-inspired optimization techniques present adaptive mechanisms that permit the understanding of complex data and altering environments. This book is a voluminous collection for the confront faced by the healthcare institutions and hospitals for practical analysis, storage, and data analysis. It explores the distinct nature-inspired optimization-based approaches that are able to handle more accurate outcomes for the current biomedical and healthcare problems. In addition to providing a state-of-the-art and advanced intelligent methods, it also enlightens an insight for solving diversified healthcare problems such as cancer and diabetes.


Editors and Affiliations

  • P.G. Department of Computer Science, Maharaja Sriram Chandra Bhanja Deo (MSCB) University, Baripada, India

    Janmenjoy Nayak

  • Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Howrah, India

    Asit Kumar Das

  • Department of Computer Applications, Veer Surendra Sai University of Technology, Sambalpur, India

    Bighnaraj Naik

  • Systems Science and Informatics Unit, Indian Statistical Institute(ISI), Bangalore, India

    Saroj K. Meher

  • Department of Information Technology and Cybersecurity, Missouri State University, Springfield, USA

    Sheryl Brahnam

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