The book is essential for anyone exploring the forefront of healthcare innovation, as it offers a thorough exploration of transformative data-driven methodologies that can significantly enhance patient outcomes and clinical efficiency in today’s evolving medical landscape.
Table of ContentsPreface
Part 1: Foundations of Medical Analytics
1. Exploring Trends in Depression and Anxiety Using Machine and Deep Learning ModelsGarvit Jakar, Timothy George, Parvathi R., Pattabiraman V. and Xiaohui Yuan
1.1 Introduction
1.2 Exploratory Data Analysis
1.2.1 Exploratory Data Analysis (EDA)
1.2.2 Interactive Visualizations with Plotly
1.2.3 Data Cleaning and Transformation
1.2.4 Chart Creation with Matplotlib and Seaborn
1.2.5 Documentation and Report Writing
1.3 Problem Statement and Motivation
1.4 Literature Survey
1.5 Data Visualization
1.5.1 Integration of Machine Learning-Powered Insights
1.5.2 Dynamic Time-Series Visualizations with Plotly
1.5.3 Fusion of Art and Data with Seaborn Styling
1.5.4 Ethical Considerations in Visual Storytelling
1.5.5 Seamless Collaboration Through Git Version Control
1.6 Overview of Dataset
1.6.1 Dataset Acquisition Links
1.6.2 Household Pulse Survey
1.7 Methodology
1.7.1 Data Selection and Attributes
1.7.2 Data Pre-Processing
1.8 Modules
1.8.1 Linear Regression
1.8.2 K-Means Clustering
1.8.3 Random Forest
1.8.4 Simple Convolutional Neural Network (CNN)
1.8.5 MobileNetV2
1.8.6 Basic Visualizations
1.9 Results and Discussion
1.10 Conclusion
References
Part 2: Disease Detection and Diagnosis
2. An Innovative Framework for the Detection and Classification of Breast Cancer Disease Using Logistic Regression Compared with Back Propagation Neural NetworkK. Reema Sekhar and Ashley Thomas
2.1 Introduction
2.2 Materials and Methods
2.2.1 Back Propagation Neural Network (BPNN)
2.2.2 Logistic Regression (LR)
2.2.3 Statistical Analysis
2.3 Results
2.4 Discussion
2.5 Conclusion
References
3. An Approach to Conduct the Diabetes Prediction Using AdaBoost Algorithm Compared with Decision Tree Classifier AlgorithmP. Jaswanth Reddy and R. Thalapathi Rajasekaran
3.1 Introduction
3.2 Materials and Methods
3.2.1 AdaBoost Algorithm
3.2.1.1 Procedure for AdaBoost Algorithm
3.2.1.2 Decision Tree
3.2.1.3 Statistical Analysis
3.3 Results and Discussion
3.4 Conclusion
References
4. Efficient Net V2-Based Pneumonia Detection: A Comparative Study with Transfer Learning ModelsSuguna M., Shane V. Jose, Om Kumar C.U., Gunasekaran T. and Prakash D.
4.1 Introduction
4.2 Related Works
4.3 Materials and Methods
4.3.1 Dataset Details
4.3.2 CLAHE
4.3.3 Data Augmentation
4.3.4 Transfer Learning Models
4.3.4.1 VGG-16
4.3.4.2 Xception
4.3.4.3 EfficientNetV2
4.4 Results and Discussion
4.4.1 Ensemble Model
4.5 Conclusion and Future Work
References
5. A Histogram Equalized Median Filtered SIFT–EfficientNet Based on Deep Learning Approach for Lung Disease DetectionSuguna M., Pujala Shree Lekha, Om Kumar C.U., Arunmozhi M. and Prakash D.
5.1 Introduction
5.2 Related Works
5.3 Materials and Methods
5.3.1 Dataset
5.3.2 Methodology: HMS-E
5.3.3 Pre-Processing
5.3.4 Histogram Equalization
5.3.5 Median Filter
5.3.6 Feature Extraction
5.3.6.1 Detection of Scale-Space Extrema
5.3.6.2 Localization of Key Points
5.3.6.3 Generation of Key Point Descriptors
5.3.7 Deep Learning Model
5.3.7.1 EfficientNetB0
5.4 Performance Measure
5.5 Results and Discussion
5.6 Conclusion and Future Work
References
Part 3: Predictive Analytics in Healthcare
6. Comparing the Efficiency of ResNet-50 and Convolutional Neural Networks for Facial Mask DetectionShaik Khaleel Basha and K. Nattar Kannan
6.1 Introduction
6.2 Materials and Methods
6.3 ResNet-50 Architecture
6.4 Convolutional Neural Networks (CNN)
6.5 Statistical Analysis
6.6 Results and Discussion
6.7 Conclusion
References
7. Enhancing Accuracy in Predicting Knee Osteoarthritis Progression Using Kellgren–Lawrence Grade Compared with Deep Convolutional Neural NetworkSai Srinivasa and Malarkodi K.
7.1 Introduction
7.2 Materials and Methods
7.2.1 Kellgren–Lawrence Grade
7.2.2 Deep Convolutional Neural Network
7.2.3 Statistical Analysis
7.3 Results and Discussion
7.3.1 Accuracy and Loss Analysis
7.3.2 Group Statistical Analysis
7.3.3 Independent Sample T-Test Results
7.3.4 Performance Comparison
7.3.5 Discussion
7.4 Conclusion
References
8. A Comparative Analysis of Support Vector Machine over K-Neighbors Classifier for Predicting Hospital Mortality with Improved AccuracyPrabhu Kumar Adi and C. Anitha
8.1 Introduction
8.1.1 Integration of Present-On-Admission Indicators
8.1.2 Challenges in Implementing Machine Learning Models
8.1.3 Predicting in-Hospital Mortality in ICU Patients
8.1.4 Machine Learning Algorithms in Mortality Prediction
8.1.5 Ethical and Operational Considerations
8.2 Materials and Methods
8.2.1 Software and Hardware Configuration
8.2.2 SVM Algorithm
8.2.3 K-Nearest Neighbor Algorithm
8.2.4 Statistical Analysis
8.3 Results and Discussion
8.3.1 Accuracy and Loss Analysis
8.3.2 Independent Sample T-Test Analysis
8.3.3 Comparison of SVM and KNN
8.3.4 Real-World Application and Implications
8.3.5 Limitations and Future Scope
8.4 Conclusion
References
9. Asthma Prediction Using Vowel Inspiration: A Machine Learning ApproachSandhya Prasad, Anik Bhaumik, Suvidha Rupesh Kumar, Rama Parvathy L., Heshalini Rajagopal and Janani S.
9.1 Introduction
9.2 Literature Survey
9.3 Motivation and Background
9.4 Proposed Method
9.4.1 Vowel Inspiration
9.4.2 Voice Activity Detection
9.4.3 Inspiration Filtering
9.4.4 Feature Extraction
9.4.4.1 Pause Frequency
9.4.4.2 Average Phonation Time
9.4.4.3 Vocalization-to-Inhalation Ratio
9.4.4.4 Inspiration Sound Energy
9.4.5 Classification
9.4.6 Praat Parameters
9.4.6.1 Jitter
9.4.6.2 NHR
9.4.6.3 HNR
9.5 Discussion
9.5.1 Classification for First Method
9.5.1.1 Logistic Regression
9.5.1.2 Naïve Bayes
9.5.1.3 K-Nearest Neighbor
9.5.1.4 Support Vector Machine
9.5.1.5 Decision Tree
9.5.1.6 Random Forest
9.5.1.7 Logistic Regression with Random Forest
9.5.2 Classification for Second Method
9.5.2.1 K-Nearest Neighbor with Classification Report
9.5.2.2 Logistic Regression with Discussion on Classification Report
9.5.2.3 Decision Tree with Discussion on Classification Report
9.5.2.4 Naïve Bayes with Discussion on Classification Report
9.6 Results
9.7 Conclusion
References
Part 4: Medical Data Analysis and Security
10. Improvement of Accuracy in Prevention of Medical Images from Security Threats Using Novel Lasso Regression in Comparison with K-Means ClassifierK. Raghul and M. Kalaiyarasi
10.1 Introduction
10.2 Materials and Methods
10.2.1 Dataset Variables
10.2.2 Machine Learning Algorithms: Novel Lasso Regression and K-Means Classifiers
10.2.2.1 Novel Lasso Regression
10.2.2.2 K-Means Classifier
10.2.3 Statistical Analysis
10.3 Result
10.4 Discussion
10.5 Conclusion
References
11. Renal Cancer Detection from Histopathological Images Using Deep LearningAkhil Kumar, R. Krithiga, S. Suseela, B. Swarna and T. Karthikeyan
11.1 Introduction
11.1.1 Motivation
11.2 Materials and Methods
11.2.1 Dataset Used
11.2.2 Models Used
11.3 Results and Discussions
11.3.1 High Classification Accuracy
11.3.2 Strong Performance on Classification Metrics
11.3.3 Fuhrman Grade Classification
11.4 Conclusion and Future Work
References
12. A Novel Method to Predicting Tumor in Fallopian Tube Using DenseNet Over Linear Regression with Enhanced EfficiencyHarish C.M. and Terrance Frederick Fernandez
12.1 Introduction
12.2 Materials and Methods
12.2.1 DenseNet Algorithm
12.2.1.1 Pseudocode for DenseNet
12.2.2 Linear Regression
12.2.2.1 Pseudocode for Linear Regression
12.2.3 Statistical Analysis
12.3 Results and Discussion
12.3.1 Accuracy and Loss Comparison
12.3.2 Statistical Analysis Results
12.3.3 Group Statistical Analysis
12.3.4 Discussion
12.3.5 DenseNet’s Performance Advantages
12.3.6 Linear Regression’s Limitations
12.3.7 Significance of Statistical Analysis
12.3.8 Limitations and Future Work
12.4 Conclusion
References
13. Protected Medical Images Against Security Threats Using Lasso Regression and K-Means AlgorithmsN. Sainath Reddy and S. Tamilselvan
13.1 Introduction
13.2 Materials and Methods
13.3 K-Means Classifier
13.4 Procedure for K-Means Classifier
13.5 Lasso Regression
13.6 Procedure for Lasso Regression
13.7 Statistical Analysis
13.8 Results
13.9 Discussion
13.10 Conclusion
References
Part 5: Emerging Trends and Technologies
14. Predicting the Factors Influencing Alcoholic Consumption of Teenagers Using an Optimized Random Forest Classifier in Comparison with Logistic RegressionDevineni Giri and M. Gunasekaran
14.1 Introduction
14.2 Materials and Methods
14.3 Random Forest Classifier
14.4 Algorithm for Random Forest Classifier
14.5 Logistic Regression Classifier
14.6 Algorithm for Logistic Regression Classifier
14.7 Results
14.8 Discussion
14.9 Conclusion
References
15. Harnessing Food Waste Potential: Advancing Protein Sequence Motif Analysis with Novel Cluster Sequence Analyzer Machine Learning ModelU. Vignesh, Geetha S. and Benson Edwin Raj
15.1 Introduction
15.1.1 Motif Regions
15.2 Suffix Tree
15.3 Clustering Algorithms in PPI
15.4 Classification Agorithms in PPI
15.5 CSA and PPI Interaction Results
15.6 Conclusion
Bibliography
16. “Hi-Tech People, Digitized HR— Are We Missing the Humane Link?”—Use of People Analytics as an Effective HRM Tool in a Selected Healthcare SectorRana Bandyopadhyay and Aniruddha Banerjee
16.1 Introduction
16.2 Research Background
16.3 Literature Review
16.4 Research Gaps
16.5 Research Methodology
16.6 Objectives
16.7 NH Success Story
16.8 Analysis and Discussion
16.9 Findings
16.10 People Analytics and Humane Touch
16.11 Conclusions
References
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