Preface xix Acknowledgment xxv 1 Advancements in Machine Learning Techniques for Biological Data Analysis 1 S. Kanakaprabha, G. Ganesh Kumar, Y. Padma, Gangavarapu and Venkata Nagaraju Thatha 1.1 Introduction 1 1.1.1 Significance of Advanced Data Analysis in Biology 2 1.2 Literature Survey 3 1.
3 Machine Learning Fundamentals 5 1.3.1 Supervised, Unsupervised, and Semi-Supervised Learning 6 1.3.2 Feature Engineering and Selection 6 1.3.3 Deep Learning Architectures for Biological Data 7 1.4 Genomic Sequence Analysis 7 1.
4.1 DNA Sequence Classification and Prediction 8 1.4.2 Genomic Variant Analysis with Machine Learning 8 1.4.3 Enhancing Epigenetic Studies through AI 8 1.5 Proteomic Profiling and Structural Prediction 9 1.5.
1 Protein Structure Prediction Using Deep Learning 10 1.5.2 Peptide and Protein Identification via Machine Learning 11 1.5.3 Functional Annotation of Proteins 11 1.6 Metabolomics and Pathway Analysis 12 1.6.1 Metabolite Identification and Quantification 14 1.
6.2 Metabolic Pathway Reconstruction Using AI 14 1.6.3 Integrative Analysis of Multi-Omics Data 15 1.7 Medical Applications 15 1.7.1 Disease Diagnosis and Biomarker Discovery 15 1.7.
2 Personalized Treatment and Drug Discovery 16 1.7.3 Predictive Modeling for Clinical Outcomes 16 1.7.4 Drug Repurposing and Adverse Event Prediction 17 1.7.5 Neuroinformatics and Brain Disorders 17 1.8 Challenges and Future Directions 17 1.
8.1 Interpretable Machine Learning in Biology 21 1.8.2 Addressing Data Privacy and Ethics 21 1.8.3 Advancing Quantum Computing in Biological Data Analysis 22 1.8.4 Handling Heterogeneous and Multi-Modal Data 22 1.
8.5 Small Data and Imbalanced Datasets 22 1.8.6 Clinical Adoption and Validation 22 1.8.7 Ethical and Societal Implications 23 1.9 Conclusion 23 1.9.
1 Synthesis of Key Contributions and Insights 23 1.9.2 Anticipated Transformations in Biological Research 24 References 24 2 Predictive Analytics in Medical Diagnosis 27 Vivek Upadhyaya 2.1 Introduction to Predictive Analytics in Healthcare 28 2.1.1 Definition of Predictive Analytics 28 2.1.2 The Significance of Predictive Analytics in Medical Diagnosis 29 2.
2 Overview of the Chapter''s Structure 29 2.3 Data Sources and Data Preprocessing 30 2.3.1 Types of Data Sources (Electronic Health Records, Wearable Devices, Genetic Data, etc.) 31 2.4 Data Quality and Cleaning 33 2.4.1 Feature Selection and Engineering 33 2.
4.2 Dealing with Missing Data 35 2.5 Predictive Analytics Techniques 36 2.5.1 Regression Analysis 36 2.5.2 Classification Models (e.g.
, Logistic Regression, Decision Trees, Random Forests) 37 2.5.3 Machine Learning Algorithms (e.g., Support Vector Machines, Neural Networks) 39 2.5.4 Time Series Analysis 40 2.6 Use Cases in Medical Diagnosis 40 2.
6.1 Early Detection of Diseases (e.g., Cancer, Diabetes) 42 2.6.2 Risk Assessment and Stratification 42 2.6.3 Personalized Treatment Recommendations 43 2.
6.4 Image Analysis and Medical Imaging 43 2.6.5 Disease Progression Tracking 46 2.6.6 Model Interpretability and Explainability 47 2.6.7 The Importance of Model Interpretability in Healthcare 47 2.
6.8 Techniques for Making Predictive Models More Interpretable 48 2.6.9 Regulatory Considerations (e.g., GDPR, HIPAA) 49 2.6.10 Ethical and Legal Considerations 50 2.
7 Challenges and Limitations 51 2.7.1 Data-Related Challenges (Data Volume, Quality, Interoperability) 53 2.7.2 Overfitting and Model Generalization 53 2.7.3 Addressing Bias and Fairness in Predictive Models 54 2.7.
4 Successful Implementation and Case Studies 55 2.7.5 Real-World Examples of Healthcare Institutions Successfully Using Predictive Analytics 56 2.8 Future Trends and Innovations 58 2.8.1 The Role of Artificial Intelligence and Deep Learning 59 2.8.2 Integration with Electronic Health Records and Telemedicine 60 2.
8.3 The Potential Impact of Quantum Computing on Medical Diagnosis 60 2.9 Conclusion 62 References 63 3 Skin Disease Detection and Classification 67 M. Aamir Gulzar, Salman Iqbal, Akhtar Jamil, Alaa Ali Hameed and Faezeh Soleimani 3.1 Introduction 68 3.2 Related Work 69 3.3 Data 70 3.4 Methodology 71 3.
4.1 Data Pre-Processing 71 3.4.2 Image Enhancement 72 3.4.3 Feature Extraction 73 3.4.4 Machine Learning Algorithm Used 74 3.
5 Results 81 3.5.1 Experimental Setup 81 3.5.2 Data Preprocessing, Feature Extraction, and Model Selection 83 3.5.3 Evaluation Metrics 85 3.5.
4 Classification and Outcomes 86 3.6 Conclusion 89 3.7 Future Work 90 References 91 4 Computer-Aided Polyp Detection Using Customized Convolutional Neural Network Architecture 93 Palak Handa, Nidhi Goel, S. Indu and Deepak Gunjan 4.1 Introduction 94 4.2 Related Works 96 4.3 Materials and Methods 96 4.3.
1 Description of the Used Datasets and Their Preparation 96 4.3.2 Data Augmentation 96 4.3.3 Customized CNN 97 4.4 Results and Discussion 98 4.4.1 CNN Optimizers 99 4.
4.2 Kernel Initializers 99 4.4.3 Color Space 100 4.4.4 Image Dimension 101 4.4.5 Kernel Size 101 4.
4.6 Sample Maps of the CNN Features 103 4.4.7 Ablation Study 104 4.4.8 Comparison of the Proposed Architecture with Existing Deep-Learning Algorithms in This Field 104 4.5 Conclusion and Future Scope 105 References 106 5 Computational Intelligence Induced Risk in Modern Healthcare: Classical Review and Current Status 109 Nitish Ojha and Shrikant Ojha 5.1 Introduction 110 5.
2 People-Based Risk 113 5.3 Doctor-Induced Risk 116 5.4 Patient-Based Risk 120 5.5 Process-Based Risk 121 5.6 Technology-Based Risk 129 5.7 Conclusion 138 References 139 6 A Hybrid Deep Learning Framework to Diagnose Sleep Apnea Using Electrocardiogram Signals for Smart Healthcare 145 Sampoorna Poria, Ahona Ghosh, Biswarup Ganguly and Sriparna Saha 6.1 Introduction 146 6.2 Proposed Methodology 148 6.
2.1 Introduction to the Data Acquisition Device 148 6.2.2 Preprocessing Using Discrete Wavelet Transform 148 6.2.3 Feature Extraction Using Auto Encoder 149 6.2.4 Classification Using Bidirectional LSTM 150 6.
3 Experiment Results and Discussions 152 6.3.1 Dataset Details 152 6.3.1.1 Preprocessing Outcomes 153 6.3.2 Feature Extraction Outcomes 154 6.
3.3 Classification Results 155 6.3.4 Statistical Validation 156 6.3.5 Experimental Setup for Computer Aided Diagnosis System 158 6.3.6 Performance Evaluation 158 6.
4 Conclusion and Future Scope 160 Acknowledgments 160 References 160 7 Deep Ensemble Feature Extraction Based Classification of Bleeding Regions Using Wireless Capsule Endoscopy Images 163 Srijita Bandopadhyay, Kyamelia Roy, Sheli Sinha Chaudhuri, Soumen Banerjee and Korhan Cengiz 7.1 Introduction 164 7.2 Related Works 164 7.3 Methodology 166 7.3.1 Dataset 167 7.3.2 Image Processing 168 7.
3.3 Histogram Equalizer 169 7.3.4 Denoising 172 7.3.5 Adaptive Filtering 173 7.3.6 Augmentation 173 7.
3.7 Data Processing 175 7.3.8 Convolutional Neural Network 175 7.3.8.1 ResNet 50 175 7.3.
8.2 Vgg 16 176 7.3.8.3 Inception V 3 177 7.3.9 Feature Extraction 177 7.3.
10 Feature Reconstruction 178 7.3.11 Classification 179 7.4 Results and Discussion 180 7.5 Conclusion 189 References 189 8 Advances in Brain Tumor Detection and Localization: A Comprehensive Survey 195 Krishnangshu Paul, Arunima Patra and Prithwineel Paul 8.1 Introduction 195 8.2 Background Study on Various Methods 198 8.2.
1 Svm 198 8.2.1.1 Advantages 198 8.2.1.2 Limitations 199 8.2.
2 Knn 199 8.2.2.1 Advantages 199 8.2.2.2 Limitations 199 8.2.
3 Logistic Regression 200 8.2.3.1 Advantages 200 8.2.3.2 Limitations 200 8.2.
4 Cnn 200 8.2.4.1 Advantages 201 8.2.4.2 Limitations 201 8.3 Methodology 202 8.
4 Experimentation 205 8.4.1 Dataset 205 8.4.2 Results Achieved 206 8.5 Discussion 210 8.6 Conclusion 210 8.6.
1 Future Scope 210 References 211 9 Integrating Apriori Algorithm with Data Mining Classification Techniques for Enhanced Primary Tumor Prediction 213 Khalid Mahboob, Nida Khalil, Fatima Waseem and Abeer Javed Syed 9.1 Overview 214 9.1.1 Feature Selection 216 9.1.2 Hyperparameter Tuning 216 9.1.3 Enhanced Primary Tumor Prediction 217 9.
1.4 Continuous Improvement 217 9.1.5 Clinical Integration 217 9.2 Previous Studies on Tumor Prediction Using Data Mining and Apriori Algorithm 218 9.3 Data Mining Process 220 9.3.1 Data Collection and Pre-Processing 221 9.
3.1.1 Data Cleaning 221 9.3.1.2 Data Transformation 221 9.3.1.
3 Data Reduction 221 9.3.1.4 Data Integration 222 9.3.1.5 Data Discretization 222 9.3.
2 Model(s) Selection and Building 222 9.3.2.1 Supervised Learning 222 9.3.2.2 Unsupervised Learning 223