Tracking and Preventing Diseases with Artificial Intelligence (Intelligent Systems Reference Library, 206)

✍ Scribed by Mayuri Mehta (editor), Philippe Fournier-Viger (editor), Maulika Patel (editor), Jerry Chun-Wei Lin (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 266
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This book presents an overview of how machine learning and data mining techniques are used for tracking and preventing diseases. It covers several aspects such as stress level identification of a person from his/her speech, automatic diagnosis of disease from X-ray images, intelligent diagnosis of Glaucoma from clinical eye examination data, prediction of protein-coding genes from big genome data, disease detection through microscopic analysis of blood cells, information retrieval from electronic medical record using named entity recognition approaches, and prediction of drug-target interactions.

The book is suitable for computer scientists having a bachelor degree in computer science. The book is an ideal resource as a reference book for teaching a graduate course on AI for Medicine or AI for Health care. Researchers working in the multidisciplinary areas use this book to discover the current developments. Besides its use in academia, this book provides enough details about the state-of-the-art algorithms addressing various biomedical domains, so that it could be used by industry practitioners who want to implement AI techniques to analyze the diseases. Medical institutions use this book as reference material and give tutorials to medical experts on how the advanced AI and ML techniques contribute to the diagnosis and prediction of the diseases.

✦ Table of Contents

Preface
Contents
Contributors
Abbreviations
1 Stress Identification from Speech Using Clustering Techniques
1.1 Introduction
1.2 Related Work
1.3 Stress Identification System Setup
1.3.1 Signal Aquisition and Pre-processings
1.3.2 Speech Feature Extraction
1.3.3 Support Vector Machine (SVM)
1.4 Implementation and Results
1.5 Conclusion
References
2 Comparative Study and Detection of COVID-19 and Related Viral Pneumonia Using Fine-Tuned Deep Transfer Learning
2.1 Introduction
2.2 Literature Review
2.2.1 The COVID-19 Coronavirus
2.2.2 COVID-19 Clinical Features
2.2.3 Related Works on the Detection of COVID-19
2.3 Methodology
2.3.1 Dataset Description
2.3.2 The VGGNet Architecture
2.4 Experimentation and Results
2.4.1 Evaluation of Results
2.5 Conclusion
References
3 Predicting Glaucoma Diagnosis Using AI
3.1 AI in Medical Diagnosis
3.2 AI in Ophthalmology Diagnosis
3.3 Artificial Intelligent Techniques in Glaucoma Diagnosis
3.4 Ensemble Method for Classification
3.5 Ensemble FGLAUC-99
3.6 Results and Discussions
3.7 Conclusion
References
4 Diagnosis and Analysis of Tuberculosis Disease Using Simple Neural Network and Deep Learning Approach for Chest X-Ray Images
4.1 Introduction
4.2 Related Work
4.3 Proposed Methodology of Neural Network (NN)-Based Approach of TB Disease Classification
4.3.1 Image Preprocessing
4.3.2 Image Segmentation
4.4 Feature Extraction
4.4.1 Classification
4.5 Result Analysis of Proposed NN Based TB Disease Classification
4.6 Deep Learning Approach of TB Disease Classification
4.6.1 Data Collection
4.6.2 Network Architecture
4.6.3 Experiments and Results Discussion
4.6.4 Experimental Setup and Evaluation
4.7 Conclusion
References
5 Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction
5.1 Introduction
5.2 Background: Common Machine Learning Algorithms and Public Referenced Genome Databases for Bioinformatics Tasks
5.3 Our Naive Bayes Algorithm
5.3.1 Apache Spark Framework
5.3.2 Data Preprocessing and Munging
5.3.3 Our Adaptive NBML Algorithm
5.4 Evaluation Results and Discussion
5.5 Conclusions
References
6 Microscopic Analysis of Blood Cells for Disease Detection: A Review
6.1 Introduction
6.1.1 Background
6.2 Literature Review
6.2.1 Collection and Exclusion of Articles for Review
6.2.2 Generalized Methodology of Disease Detection
6.2.3 State-of-the-Art Methods for Different Stages of Microscopic Analysis for Disease Detection
6.3 Research Gaps
6.4 Conclusion
6.5 Future Scope
References
7 Investigating Clinical Named Entity Recognition Approaches for Information Extraction from EMR
7.1 Introduction
7.2 Clinical Named Entity Recognition
7.2.1 Rule-Based Approach
7.2.2 Machine Learning-Based Approaches
7.2.3 Hybrid Approaches
7.3 Experimental Evaluation
7.3.1 spaCy NER Model
7.3.2 Conditional Random Field NER Model
7.3.3 BLSTM NER
7.3.4 BLSTM with CRF
7.4 Result Discussion
7.5 Conclusion and Future Scope
References
8 Application of Fuzzy Convolutional Neural Network for Disease Diagnosis: A Case of Covid-19 Diagnosis Through CT Scanned Lung Images
8.1 Introduction
8.2 Background Technologies
8.2.1 Fuzzy Logic
8.2.2 Convolutional Neural Network
8.2.3 Adding Fuzziness in the Neural Network
8.3 Related Work
8.4 Generic Architecture of the Disease Diagnosis System Based on Fuzzy Convolutional Neural Network
8.5 Detailed Method, Experiment, and Results
8.6 Conclusion
References
9 Computer Aided Skin Disease (CASD) Classification Using Machine Learning Techniques for iOS Platform
9.1 Introduction
9.1.1 Background on Existing System
9.2 System Analysis
9.2.1 Literature Survey on Existing System
9.2.2 Proposed CASD System
9.2.3 Requirements for CASD System
9.3 System Design
9.3.1 Create ML Model
9.3.2 Core ML Model
9.3.3 Apple iOS Architecture for CASD Machine Learning System
9.3.4 Firebase Architecture for CASD Database System
9.3.5 Dataset Images for CASD System
9.3.6 Database Structure of CASD System
9.4 System Implementation
9.4.1 Four View Controllers of CASD System
9.4.2 Skin Lesion Classifier Model of CASD System
9.4.3 Steps of Model Creation
9.4.4 Lıve Testing Output Using the Developed CASD System in iOS Platform
9.5 Conclusion and Future Scope
References
10 A Comprehensive Study of Mammogram Classification Techniques
10.1 Introduction
10.2 Mammography and Mammogram Datasets
10.3 Related Works in Classification of Lesions Using Data Mining Techniques
10.4 Techniques for Classifying Mammogram Images
10.4.1 Function-Based Methods
10.4.2 Probability-Based Methods
10.4.3 Similarity-Based Methods
10.4.4 Rule-Based Methods
10.5 Challenges in Classifying Mammogram Images
10.5.1 Dataset Related Challenges
10.5.2 Classification Techniques Related Challenges
10.6 Techniques to Improve Performance of Machine Learning Models
10.7 Towards Deep Learning
10.8 Conclusion
References
11 A Comparative Discussion of Similarity Based Techniques and Feature Based Techniques for Interaction Prediction of Drugs and Targets
11.1 Introduction
11.2 Similarity Based Techniques
11.2.1 Neighborhood Models
11.2.2 Bipartite Local Models
11.2.3 Network Diffusion Models
11.2.4 Matrix Factorization Models
11.3 Feature Based Techniques
11.3.1 SVM Based Models
11.3.2 Ensemble Based Models
11.3.3 Miscellaneous Models
11.4 Comparison of Similarity Based and Feature Based Techniques
11.5 Conclusion
References