Intro
Table of Contents
About the Author
About the Technical Reviewer
Chapter 1: An Introduction to Artificial Intelligence in Medical Sciences and Psychology
Context of the Book
The Book's Central Point
Artificial Intelligence Subsets Covered in this Book
Structure of the Book
Tools Used in This Book
Python Distribution Package
Anaconda Distribution Package
Jupyter Notebook
Python Libraries
Encapsulating Artificial Intelligence
Implementing Algorithms
Supervised Algorithms
Unsupervised Algorithms
Artificial Neural Networks
Conclusion

Chapter 2: Realizing Patterns in Diseases with Neural Networks
Classifying Cardiovascular Disease Diagnosis Outcome Data by Executing a Deep Belief Network
Preprocessing the Cardiovascular Disease Diagnosis Outcome Data
Debunking Deep Belief Networks
Designing the Deep Belief Network
Relu Activation Function
Sigmoid Activation Function
Training the Deep Belief Network
Outlining the Deep Belief Network's Predictions
Considering the Deep Neural Network's Performance
Accuracy Fluctuations Across Epochs in Training and Cross-Validation

Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
Classifying Diabetes Diagnosis Outcome Data by Executing a Deep Belief Network
Executing a Deep Belief Network to Classify Diabetes Diagnosis Outcome Data
Outlining the Deep Belief Network's Predictions
Considering the Deep Neural Network's Performance
Accuracy Fluctuations Across Epochs in Training and Cross-Validation
Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation
Conclusion

Chapter 3: A Case for COVID-19: Considering the Hidden States and Simulation Results
Executing the Hidden Markov Model
Descriptive Analysis
Carrying Out the Gaussian Hidden Markov Model
Considering the Hidden States in US Confirmed COVID-19 Cases with the Gaussian Hidden Markov Model
Simulating US Confirmed COVID-19 Cases with the Monte Carlo Simulation Method
US Confirmed COVID-19 Cases Simulation Results
Conclusion
Chapter 4: Cancer Segmentation with Neural Networks
Exploring Cancer
Exploring Skin Cancer

Classifying Patient Skin Cancer Outcomes by Executing a CNN
A CNN Pipeline
A CNN's Architectural Structure
Classifying Skin Cancer Diagnosis Image Data by Executing a CNN
Preprocessing the Training Skin Cancer Image Data
Preprocessing the Validation Skin Cancer Image Data
Generating the Training Skin Cancer Diagnosis Image Data
Tuning the Training Skin Cancer Image Data
Executing the CNN to Classify Skin Cancer Diagnosis Image Data
Considering the CNN's Performance
Accuracy Fluctuations Across Epochs in Training and Cross-Validation