Predictive Analytics of Psychological Disorders in Healthcare: Data Analytics on Psychological Disorders (Lecture Notes on Data Engineering and Communications Technologies)

Preface
Contents
Editors and Contributors
Predictive Analysis of Psychological Disorders on Health
	1 Introduction
	2 Classification
	References
The Rising Aesthetic Concern with Digitalization: Qualitative Evidences from Turkey
	1 Introduction
		1.1 Research Design
	2 Digitalization, Aesthetic Concern, and Aesthetic Surgery
	3 Turkish Cases: Social Media, Aesthetic Concern and Fake News
		3.1 Descriptive Findings
		3.2 A Model: The Link Between Social Media, Aesthetic Concern, and the Desire of Aesthetic Surgery
	4 Conclusion
	References
AI-Based Predictive Analytics for Patients’ Psychological Disorder
	1 Introduction
	2 The Person at Risk of Psychological Disorder
	3 Classification of Psychological Disorders
	4 Conclusion
	References
Monitoring the Impact of Stress on Facial Skin Using Affective Computing
	1 Introduction
	2 Background
		2.1 Dimensional Conceptualization of Emotion
		2.2 Facial Action Coding System (FACS)
		2.3 Facial Skin Conditions Due to the Stress Responses
	3 Literature Review
	4 Methodology
	5 Experimental Results and Analysis
		5.1 Material Used
		5.2 Results of the Stress in Valence-Arousal (V-A) Space
		5.3 Experimental Results for Facial Skin Conditions
		5.4 Overall Evaluation of the Proposed Approach
	6 Conclusion
	References
Computational Techniques in Prognostic and Data Modelling of Mentally Ill Patients with Special Emphasis on Post-COVID-19 Scenario
	1 Introduction
		1.1 Factors Affecting Mental Health
	2 Role of Computational Models in Mental Healthcare
	3 Role of Data Analytical Tools in Mental Healthcare
	4 Post-Covid Scenario
	5 Pilot Study
	6 Role of Prognostic Modelling in Mental Healthcare
	7 Tele-Healthcare Services in Post-Covid Scenario
	8 Conclusion and Future Prospects
	References
Predicting Depression Through Social Media
	1 Introduction
		1.1 Social Media Platforms
		1.2 Depression
		1.3 Relationship Between Depression and Social Media
	2 Methods for Prediction
	3 Sources of Data
		3.1 Survey Responses
		3.2 Self-declared Mental Health Status
		3.3 Forum Data
		3.4 Annotated Posts
	4 Future Studies
	5 Ethical Issues
	6 Conclusion
	References
COVID-19 Impact on Online Learning: A Statistical and Machine Learning Model Analysis for Stress Detection
	1 Introduction
	2 Predictive Analytics and Its Significance
	3 Predictive Modeling Approaches
		3.1 Tree-Based Analysis
		3.2 Random Forest Technique
		3.3 Artificial Neural Networks
		3.4 Stepwise Regression
	4 Ensembles of Models: Prediction Analytics
		4.1 Probabilistic Neural Networks (PNN)
		4.2 Nonlinear AutoRegressive Network with eXogenous Inputs (NARX)
		4.3 Support Vector Machine (SVM)
		4.4 Long Short-Term Memory Networks (LSTM)
		4.5 Multilayer Perceptron (MLP)
		4.6 Least-Squares Boosting
	5 Statistical Models and Analysis
		5.1 ANOVA and MANOVA: Analysis of Variance
		5.2 Mann-Whitney and Kruskal-Wallis Test
		5.3 Chi-square Test
		5.4 Structural Equation Modeling
	6 Predictive Analysis: Stress Detection Among Design and Technology Student
		6.1 Participants
		6.2 Procedure
		6.3 Measures
		6.4 Analysis and Results
		6.5 Discussion
	7 Conclusions
	References
Measuring Mental Health at Workplaces Using Machine Learning Techniques
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Linear Regression
		3.2 Logistic Regression
		3.3 K Nearest Neighbor (KNN)
		3.4 Decision Tree
		3.5 Random Forest
		3.6 Gradient Boosting
		3.7 Adaptive Boosting
	4 Results
	5 Conclusion
	References
Old Age People Emotional Stress Prediction During Outbreak Using Machine Learning Methods
	1 Introduction
	2 Outbreak
		2.1 Major Outbreaks of the World
		2.2 Corona Virus
	3 Stress
		3.1 Types of Stress
		3.2 Symptoms of Emotional Stress in Old People
		3.3 There Are a Plethora of Reasons for the Elevation of Emotional Stress; Here We Are Explaining Only Those Which Affect Old Age People During Outbreaks
	4 Methodology
		4.1 Participant
		4.2 Parameters
		4.3 Algorithms
		4.4 Mathematical Formula
		4.5 Flow Diagram
	5 Result and Discussion
		5.1 Comparative Study Between Algorithms
		5.2 Accuracy and Error
	6 Conclusion
	References
Unlocking the Psychological Toolbox: To Transform or to Sustain
	1 Introduction
	2 Reactions to Psychosocial Stressors During the Pandemic
	3 Conceptual Model
	4 Rationale
	5 Design of the Intervention
		5.1 Understand Distress and Its Nature
		5.2 Developing Healthy Coping Strategies
		5.3 Maintaining Psychological Health
	6 Conclusion, Limitations, and Future Direction
	References
Sentimental Analysis of Fears, Psychological Disorders and Health Issues Through NVIVO During Second Wave of Covid-19
	1 Introduction
	2 Literature Review
	3 Objectives
	4 Results and Discussion
		4.1 Research Methods
		4.2 Preliminary Tests
		4.3 Correlation
		4.4 Word Clouds Analysis
		4.5 Sentimental Analysis
		4.6 Thematic Analysis
	5 Conclusion
	6 Future Work
	References
Tertiary Students Stress Detection During Online Learning in Jos, Nigeria
	1 Introduction
	2 Conceptual Clarification on Stress Among Students in Online Learning
	3 Possible Stressors of Students in Online Learning
	4 Theoretical Framework of Stress Among Students in Online Learning
	5 Methods
		5.1 Sample of Study
	6 Measures of Stress Perceived by Situation
	7 Procedures
	8 Result
	9 Discussion
	10 Conclusion
	References
Prediction of Mental Health in Cancer Patients Using Ensemble Machine Learning
	1 Introduction
	2 Related Work
	3 Materials and Method
	4 Results
	5 Conclusion
	References
Alzheimer’s Disease Classification Using Feed Forwarded Deep Neural Networks for Brain MRI Images
	1 Introduction
	2 Subjects and MRI Scans
	3 Methods
		3.1 Data Pre-processing
		3.2 Data Segregation
		3.3 Modeling
		3.4 Model Training
		3.5 Classification Models
	4 Results and Discussion
	5 Conclusions
	References
Challenges and Privacy Concerns Related to Use of Information Technology in Mental Healthcare
	1 Introduction
	2 Challenges for Maintaining Mental Health
		2.1 Knowledge Barriers
		2.2 Attitudinal Barriers
		2.3 Structural Barriers
	3 Opportunities of Using ICT for Mental Healthcare
		3.1 Accessibility and Flexibility
		3.2 Anonymity and Privacy
		3.3 Operating with Limited Resources
		3.4 Easy Outreach and Awareness Campaigns
		3.5 Continuous Remote Monitoring
	4 Information Technology Systems Targeting Mental Health
		4.1 Online Consultation and Therapies
		4.2 Mobile Phone Applications
		4.3 Educational Toys
	5 Challenges to Offer Privacy
		5.1 Use of Smart Phone Applications
		5.2 Interests of Third Party and Simple Sharing Mechanisms
		5.3 Limited Technology Awareness of Care Providers
		5.4 Security of Devices and Infrastructures
		5.5 Limited or No Legislation
	6 Future Trends and Possibilities
		6.1 Artificial Intelligence Techniques
		6.2 Advanced Image Processing
		6.3 Identification of Reality
		6.4 Recommendation Systems
	7 Conclusion
	References