Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective

✍ Scribed by Meenu Gupta (editor), Rachna Jain (editor), Arun Solanki (editor), Fadi Al-Turjman (editor)

• Publisher: Chapman and Hall/CRC
• Year: 2021
• Tongue: English
• Leaves: 219
• Series: Chapman & Hall/CRC Internet of Things
• Edition: 1
• Category: Library

✦ Synopsis

Cancer Prediction for Industrial IoT 4.0: A Mining and Machine Learning Perspective explores the various cancers using Artificial Intelligence Techniques. It presents the rapid advancement in the existing predicting models by applying Machine Learning techniques. Several applications of Machine Learning in different cancer prediction and treatment are incorporated in the book. The wide variety of topics it presents offers readers multiple perspectives on various disciplines, including the number of chapters in the edited book.

The key features of this Edited Book are as follows:

• Fundamental, History, Reality and Challenges of Cancer

•Concepts and Analysis of different cancer in human

•Machine Learning-based Deep Learning and data mining Concepts in the prediction of cancer

•Prediction of cancer including different real-world examples

•Strategies and Tools for Prediction of cancer

•Future prospectus in cancer prediction and treatment

The main benefits of reading this book are as follows:

• Readers can be able to understand the fundamental concepts and analysis of Cancer prediction and treatment
• Readers can learn how to apply emerging technologies such as Machine Learning into practice to tackle challenges in s domains/fields of cancer with real-world scenarios.
• Help guide the reader to the specific ideas, tools, and practices most applicable to the product/service development and innovation problems and opportunities.
• Hands-on chapters contributed by the academicians and other professionals from reputed organizations provides and describes frameworks, applications, best practices and case studies on emerging cancer treatment and predictions.

This book will help the graduates, data scientists, machine learning users, doctors and analytics Managers.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
Chapter 1. Investigation of IoMT-Based Cancer Detection and Prediction
1.1 Introduction
1.2 Cancer Diagnosis and Research
1.2.1 Computational Analysis for Cancer Research
1.2.2 Role of the IoMT in Cancer Detection and Prediction
1.2.3 Role of ML/DL Techniques in Cancer Detection and Prediction
1.3 Literature Review
1.4 Proposed Methodology
1.5 Transfer Learning
1.5.1 Pre-Trained Models
1.5.2 VGG16 and VGG19
1.5.3 ResNet-50
1.5.4 DenseNet-121
1.6 Experiment Setting
1.6.1 Source of Dataset
1.6.2 Feature Extraction and Classification
1.6.3 Pre-Processing and Training
1.6.4 Model Evaluation Metrics
1.7 Results and Comparative Analysis
1.8 Summary
References
Chapter 2. Histopathological Cancer Detection Using CNN
2.1 Introduction
2.2 Major Types of Cancer Detected through Histopathology
2.3 Literature Review
2.4 Dataset
2.5 Methodology
2.5.1 Data Pre-Processing
2.5.2 Components of the Ensemble
2.5.3 Ensemble Model
2.6 Results and Discussion
2.6.1 Ensemble Components
2.7 Conclusion
References
Chapter 3. Role of Histone Methyltransferase in Breast Cancer
3.1 Introduction
3.2 Active Histone Modifications
3.3 H3K4me3: Establishment as well as Maintenance
3.4 Crosstalk between H2BK120u1, H3K4me3, and H3K79me3
3.5 H3K4me3: Histone Acetylation
3.6 H3K36me3: Histone Deacetylation
3.7 Epigenetic Regulation Mechanism
3.8 G9a Requirement: H3K9 Dimethylation
3.9 H3K9 Dimethyltransferase Activity
3.10 G9a Role: Histone and Non-histone Proteins Methylation
3.11 G9a: Methyltransferase Independent Activity
3.12 G9a Role in Immunology
3.13 G9a Interactions
3.14 G9a Inhibition: By Chemical Probes
3.15 G9a Target Dependency in Breast Cancer
References
Chapter 4. Breast Cancer Detection Using Machine Learning and Its Classification
4.1 Introduction
4.1.1 Breast Cancer
4.1.2 Importance of Early Detection of Breast Cancer
4.2 Literature Review
4.3 Machine Learning-Based Classifiers for Breast Cancer Detection
4.4 Computational Comparison of Machine Learning Techniques for Breast Cancer Detection
4.5 Conclusion
References
Chapter 5. Diagnosis and Prediction of Type-2 Chronic Kidney Disease Using Machine Learning Approaches
5.1 Introduction
5.2 Related Work
5.3 Proposed Methodology
5.3.1 Classification Accuracy Metrics
5.4 Machine Learning Techniques for Prediction of Chronic Kidney Disease
5.4.1 Support Vector Machine
5.4.2 Random Forest
5.4.3 Decision Tree
5.4.4 Logistic Regression
5.5 Framework: Chronic Kidney Dataset from the UCI and Its Algorithm
5.6 Experiments and Results
5.6.1 Accuracy by Decision Tree Classifier
5.6.2 Accuracy by SVM Classifier
5.6.3 Accuracy by Random Forest Classifier
5.6.4 Accuracy by Logistic Regression Classifier
5.6.5 Select the Best Classifier Model by its Classification Accuracy
5.7 Conclusion and Future Scope
References
Chapter 6. Behavioral Prediction of Cancer Using Machine Learning
6.1 Introduction
6.1.1 Machine Learning-Based Methods for Cancer Prediction
6.1.2 Comparison of Existing Cancer Prediction-Based Methods
6.2 Related Work and Discussion
6.3 Machine Learning-Based Prediction of Cancer Susceptibility
6.4 Machine Learning-Based Models to Predict Cancer Recurrence
6.5 Machine Learning-Based Model to Predict Cancer Survival
6.6 Conclusion
References
Chapter 7. Prediction of Cervical Cancer Using Machine Learning
7.1 Introduction
7.2 Overview of Cervical Cancer
7.2.1 Types of Cervical Cancer
7.3 Cervical Cancer Diagnosis and Treatment in India
7.4 Early Detection of Cervical Cancer and Its Importance
7.4.1 HPV and Types of Screening
7.5 Machine Learning-Based Methods to Predict Cervical Cancer
7.6 Conclusion
References
Chapter 8. Applications of Machine Learning in Cancer Prediction and Prognosis
8.1 Introduction
8.2 Machine Learning Techniques
8.3 Cancer Prediction/Prognosis Using Machine Learning
8.4 Use of Machine Learning Application in the Cancer Field
8.4.1 Cancer Susceptibility Prediction
8.4.2 Prediction of Cancer Survivability
8.4.3 Prediction of Cancer Recurrence
8.5 Conclusion and Future Scope
References
Chapter 9. Significant Advancements in Cancer Diagnosis Using Machine Learning
9.1 Introduction
9.2 Benchmark Dataset
9.3 Brain Tumor
9.4 Lung Cancer
9.5 Skin Cancer
9.6 Acute Lymphoblastic Leukemia (ALL)
9.7 Breast Cancer
9.8 Liver Cancer
9.9 Conclusion
References
Chapter 10. Human Papillomavirus and Cervical Cancer
10.1 Introduction
10.2 HPV
10.3 HPV-16 Causes
10.4 Pathogenesis
10.5 Epidemiology
10.6 Importance of HPV Virus in Oncogenesis
10.7 HPV's Role in Cervical Cancer Development
10.8 Symptoms of Cervical Cancer
10.8.1 Perceived Cervical Infection Causes
10.9 Risk Factors
10.10 Screening of Cervical Cancer
10.11 Diagnosis
10.12 HPV Vaccination
10.13 Conclusion
References
Chapter 11. Case Studies/Success Stories on Machine Learning and Data Mining for Cancer Prediction
11.1 Introduction
11.2 Literature Survey
11.2.1 Oral Cancer Detection
11.2.2 Skin Cancer Detection
11.2.3 Brain Cancer Detection
11.2.4 Ovarian Cancer Detection
11.2.5 Lung Cancer Detection
11.2.6 Breast Cancer Detection
11.3 Case Study
11.3.1 Prediction of Breast Cancer Using ML: A Case Study
11.3.2 Prediction of Lung Cancer Using ML: A Case Study
11.3.3 Prediction of Skin Cancer Using ML: A Case Study
11.3.4 Prediction of Brain Tumors Using ML: A Case Study
11.3.5 Prediction of Oral Cancer Using ML: A Case Study
11.4 Importance of Machine Learning and Data Mining in Cancer Prediction
11.5 Relation Between Data Mining and Machine Learning
11.6 Issues and Challenges with Data Mining and Machine Learning
11.7 Conclusion and Future Scope
References
Index