Smart healthcare systems / edited by Adwitiya Sinha and Megha Rathi.

Format:

Book

Contributor:

Sinha, Adwitiya, editor.

Rathi, Megha, editor.

Language:

English

Subjects (All):

Artificial intelligence--Medical applications.

Artificial intelligence.

Medical informatics.

Artificial Intelligence.

Medical Informatics.

Data Science.

Machine Learning.

Medical Subjects:

Artificial Intelligence.

Medical Informatics.

Data Science.

Machine Learning.

Physical Description:

1 online resource (249 pages)

Edition:

1st ed.

Place of Publication:

Boca Raton : CRC Press, [2019]

Summary:

About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

Preface

Editors

Contributors

1. Big Data Analytics in Healthcare

1.1 Introduction: Background and Driving Forces

1.2 Related Work

1.3 Observations

1.4 Open Challenges

1.5 Proposed Solutions

1.6 Conclusion

References

2. Smart Medical Diagnosis

2.1 Introduction: Background and Driving Forces

2.2 Description and Experimentation

2.2.1 Heart Dataset

2.2.2 Diabetes Dataset

2.2.3 Breast Cancer Dataset

2.2.4 Parkinson's Dataset

2.2.5 Kidney Dataset

2.3 Visualization

2.3.1 Heart Disease

2.3.2 Diabetes

2.3.3 Breast Cancer

2.3.4 Parkinson's Disease

2.3.5 Kidney Disease

2.4 Classification

2.4.1 Logistic Regression

2.4.2 Bayesian Logistic Regression

2.4.3 Decision Trees

2.4.4 Random Forest

2.4.5 Extreme Gradient Boosting

2.4.6 Genetic Algorithm

2.4.7 Comparison Model

2.5 Web Application

2.6 Conclusion

3. Lifestyle Application for Visually Impaired

3.1 Introduction

3.2 Related Work

3.3 Methodology

3.3.1 Text Recognition

3.3.2 Color Recognition

3.3.3 Face Recognition

3.3.4 Braille Touch Keyboard

3.3.5 Help from Volunteers

3.3.6 Object Recognition

3.3.7 Barcode to Product Description

3.4 Algorithms Used

3.4.1 Tesseract Algorithm

3.4.2 Scalable Object Detection (Deep Neural Network)

3.4.3 Single Shot Multibox Detector

3.4.4 Principle Component Analysis

3.5 Results and Findings

3.6 Conclusion and Future Work

4. Classification of Genetic Mutations

4.1 Introduction

4.2 Related Work

4.3 Dataset Collection

4.4 Methodology

4.4.1 Data Preprocessing

4.4.1.1 Natural Language Processing

4.5 Machine Learning Algorithms

4.5.1 Decision Tree

4.5.2 Random Forest

4.5.3 XGBoost.

4.5.4 Artificial Neural Networks

4.5.5 Support Vector Machine

4.5.6 Naïve Bayes

4.6 System Architecture

4.7 Block Diagram of Proposed Model

4.8 Evaluation Methods

4.9 Experimental Result

4.10 Conclusion

5. m-Health: Community-Based Android Application for Medical Services

5.1 Motivation

5.2 Background

5.3 m-Health App: Description

5.3.1 Android as a platform

5.3.2 Real-Time Database: Firebase

5.3.3 Firebase Cloud Messaging

5.3.4 GeoFire

5.3.4.1 Example Usage

5.3.5 Star Rating Prediction

5.3.5.1 Feature Vector Generation

5.3.5.2 Training and Rating Prediction

5.3.5.3 Multinomial Naive Bayes Approach

5.3.6 Flask

5.4 Implementation Results

5.4.1 Process for Results

5.5 Conclusion

5.6 Future Scope

6. Nanoemulsions: Status in Antimicrobial Therapy

6.1 Introduction

6.1.1 NEs as Novel Delivery Systems for Drugs

6.1.2 Methods of Preparation of NEs

6.1.2.1 Low-Energy Methods

6.1.2.2 High-Energy Methods

6.1.3 Comparison between the Methods

6.2 Characterization of NEs

6.3 Advantages of NEs as Antimicrobial Agents

6.4 Mechanism of Action Responsible for Antimicrobial Activity of NEs

6.5 Application of NE as Antimicrobial Agents

6.5.1 NEs as Antibacterial Agents

6.5.2 NEs as Antifungal Agents

6.5.3 NEs as Antiviral Agents

6.5.4 NEs as Antiparasitic Agents

6.6 Patents Related to NEs Having Antimicrobial Activity

6.7 Limitations of NEs

6.8 Future Prospective

6.9 Conclusion

7. Analysis of Air Quality and Impacts on Human Health

7.1 Introduction

7.2 Related Work

7.3 Materials and Methodology

7.3.1 Study Area

7.3.2 Data Collection

7.3.3 Data Preprocessing

7.3.4 Analysis

7.4 Machine Learning Techniques Used

7.4.1 Multiple Linear Regression

7.4.2 Random Forest.

7.4.3 Artificial Neural Network

7.4.4 Relationship between Pollutants and Meteorological Factor

7.4.5 Predicting AQI Using ANN Model

7.5 Data Visualization

7.5.1 Correlation Plot

7.5.2 Calendar Plot

7.5.3 Normalized Line Plot

7.6 Results

7.7 Novelty

7.8 Conclusions

8. Brain Tumor Detection and Classification in MRI: Technique for Smart Healthcare Adaptation

8.1 Introduction

8.2 Related Work

8.3 Basic Methodology

8.3.1 Preprocessing

8.3.2 Segmentation

8.3.3 Feature Extraction

8.3.4 Classification

8.3.5 Deep Learning Methodology

8.4 Datasets and Evaluation Criteria

8.4.1 Evaluation Criteria

8.5 Conclusion

9. Deep Strategies in Computer-Assisted Diagnosis and Classification of Abnormalities in Medical Images

9.1 Introduction

9.2 Trends for Deep Architecture Learning

9.3 Deep CNN Architecture Variants

9.4 Deep Architectures in Radiology for Problem-Solving

9.5 Issues, Limitations, and Dependencies on Medical Imaging Research

9.6 Discussion on the Presented Work

10. Major Histocompatibility Complex Binding and Various Health Parameters Analysis

10.1 Introduction

10.1.1 Vaccines and T-Cells

10.1.2 MHC: Class I and Class II

10.1.3 SMM: Stabilized Matrix Method

10.1.4 BLOSUM: Block Substitution Matrix

10.1.5 PMBEC: Peptide MHC Binding Energy Covariance Matrix

10.2 Literature Review

10.3 Methodology

10.4 Technology Used

10.4.1 R Language

10.4.2 Stabilized Matrix Method

10.4.3 IBM Watson

10.4.4 Shiny Apps Server

10.5 Results

10.6 Conclusion and Future Scope

11. Partial Digest Problem

11.1 Introduction: Background and Driving Forces

11.2 The Partial Digest Problem

11.3 Datasets

11.4 Existing Approaches

11.4.1 Naïve Algorithm.

11.4.1.1 Pseudocode-Naïve Algorithm

11.4.2 Improved Naïve Algorithm

11.4.2.1 Pseudocode-Improved Naïve Algorithm

11.4.3 Branch-and-Bound Algorithm

11.4.3.1 Pseudocode-Branch-and-Bound Algorithm

11.4.4 Skiena's Backtracking Algorithm

11.4.4.1 Pseudocode-Skiena's Backtracking Algorithm

11.4.5 Modern PDP Algorithms

11.4.5.1 The Breadth First Search Algorithm

11.4.5.2 The Space-Optimized Breadth First Search Algorithm

11.4.6 Computational Results

11.5 Conclusion

12. Deep Learning for Next-Generation Healthcare: A Survey of State-of-the-Art and Research Prospects

12.1 Introduction

12.2 Deep Learning

12.2.1 Motivation

12.2.2 Deep Learning Framework

12.2.3 Deep Learning Models

12.2.3.1 Stacked Autoencoders

12.2.3.2 Deep Belief Network

12.2.3.3 Deep Boltzmann Machine

12.2.3.4 Recurrent Neural Network

12.2.3.5 Convolutional Neural Networks

12.3 Application of Deep Learning in Healthcare Systems

12.3.1 Medical Imaging

12.3.2 Bioinformatics

12.3.3 Medical e-Health Records

12.3.4 Health Monitoring

12.4 Challenges and Future Research Prospects

12.5 Conclusions

13. Applications of Protein Nanoparticles as Drug Delivery Vehicle

13.1 Introduction

13.2 Characterization of Protein NPs

13.3 General Protein Used for Preparation of NPs

13.3.1 Albumin

13.3.2 Gelatin and Elastin

13.3.3 Gliadin and Legumin

13.3.4 Zein

13.3.5 Soy and Milk Protein

13.3.6 Whey Proteins

13.4 Factors Affecting Protein NP Preparation

13.5 Technique for the Preparation of Protein NPs

13.5.1 Coacervation/Desolvation Method

13.5.2 Emulsion/Solvent Extraction

13.5.3 Complex Coacervation

13.5.4 Electrospray

13.6 Toxicity of NPs

13.7 Protein NP as Diagnostic Tool

13.8 Protein NPs as Therapeutics

13.8.1 NPs for Anticancer Therapy.

13.8.2 NPs for Immunomodulation

13.8.3 NPs for Ocular Disorders

13.8.4 NPs Reported for Other Therapies

13.9 Conclusion

14. Exploring Food Domain Using Deep Neural Networks

14.1 Introduction

14.2 Challenges

14.3 Related Work

14.4 Methodology

14.4.1 Dataset

14.4.2 CNN Machine Learning Algorithm

14.4.3 R Packages Used

14.4.3.1 Keras

14.4.3.2 CARET

14.4.3.3 Shiny

14.5 Result

14.6 User Interface

14.7 Conclusion

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

ISBN:

0-429-67028-1

0-429-02057-0

0-429-67177-6

9780429020575

OCLC:

1110708621