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Machine Learning 8.4 Feature Engineering 8.5 Methodology 8.6 Result Analysis 8.7 Conclusion References 9 A Comprehensive Study on the Application of Grey Wolf Optimization for Microarray Data 9.1 Introduction 9.2 Microarray Data 9.3 Grey Wolf Optimization (GWO) Algorithm 9.4 Studies on GWO Variants 9.5 Application of GWO in Medical Domain 9.6 Application of GWO in Microarray Data 9.7 Conclusion and Future Work References 10 The Cluster Analysis and Feature Selection: Perspective of Machine Learning and Image Processing 10.1 Introduction 10.2 Various Image Segmentation Techniques 10.3 How to Deal With Image Dataset 10.4 Class Imbalance Problem 10.5 Optimization of Hyperparameter 10.6 Case Study 10.7 Using AI to Detect Coronavirus 10.8 Using Artificial Intelligence (AI), CT Scan and X-Ray 274 10.9 Conclusion References

      8  Part 3: MACHINE LEARNING AND HEALTHCARE APPLICATIONS 11 Artificial Intelligence and Machine Learning for Healthcare Solutions 11.1 Introduction 11.2 Using Machine Learning Approaches for Different Purposes 11.3 Various Resources of Medical Data Set for Research 11.4 Deep Learning in Healthcare 11.5 Various Projects in Medical Imaging and Diagnostics 11.6 Conclusion References 12 Forecasting of Novel Corona Virus Disease (Covid-19) Using LSTM and XG Boosting Algorithms 12.1 Introduction 12.2 Machine Learning Algorithms for Forecasting 296 12.3 Proposed Method 12.4 Implementation 12.5 Results and Discussion 12.6 Conclusion and Future Work References 13 An Innovative Machine Learning Approach to Diagnose Cancer at Early Stage 13.1 Introduction 13.2 Related Work 13.3 Materials and Methods 13.4 System Design 13.5 Results and Discussion 13.6 Conclusion References 14 A Study of Human Sleep Staging Behavior Based on Polysomnography Using Machine Learning Techniques 14.1 Introduction 14.2 Polysomnography Signal Analysis 14.3 Case Study on Automated Sleep Stage Scoring 14.4 Summary and Conclusion References 15 Detection of Schizophrenia Using EEG Signals 15.1 Introduction 15.2 Methodology 15.3 Literature Review 15.4 Discussion 15.5 Conclusion References 16 Performance Analysis of Signal Processing Techniques in Bioinformatics for Medical Applications Using Machine Learning Concepts 16.1 Introduction 16.2 Basic Definition of Anatomy and Cell at Micro Level 397 16.3 Signal Processing—Genome Signal Processing 403 16.4 Hotspots Identification Algorithm 16.5 Results—Experimental Investigations 16.6 Analysis Using Machine Learning Metrics 16.7 Conclusion Appendix A.1 Hotspot Identification Code A.2 Performance Metrics Code References 17 Survey of Various Statistical Numerical and Machine Learning Ontological Models on Infectious Disease Ontology 17.1

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