Cancer Prediction for Industrial IoT 4.0: A Machine Learning Perspective

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