Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases

Keyphrases

• Classification Performance 100%
• Machine Learning Techniques 100%
• Deep Learning 100%
• Machine Learning Applications 100%
• Multi-label Classification 100%
• Class Imbalance 100%
• Deep Learning Applications 100%
• Psychotic Disorders 100%
• Insomnia 50%
• Multilayer Perceptron 50%
• Bipolar Disorder 50%
• Disease Dataset 50%
• Vascular Dementia 25%
• Superior Performance 25%
• Machine Learning Models 25%
• Model Accuracy 25%
• Attention Deficit Hyperactivity Disorder 25%
• Support Vector Machine 25%
• Schizophrenia 25%
• R-value 25%
• Deep Neural Network 25%
• Classification Task 25%
• Deep Learning Model 25%
• Hidden Patterns 25%
• Patient Data 25%
• Random Forest Model 25%
• Accuracy Metrics 25%
• Electronic Health Records 25%
• Neural Machine Learning 25%
• Random Tree 25%
• Clinical Decision Systems 25%
• Multi-label Problem 25%
• Multi-label Classification Problem 25%
• Psychotic Diseases 25%
• Clinical Classification 25%
• Random Decision Trees 25%
• Deep Neural Network Learning 25%
• Strongly Correlated 25%
• Classification Perspective 25%

Computer Science

• Machine Learning Technique 100%
• Deep Learning Technique 100%
• Classification Performance 100%
• Class Imbalance 100%
• Multi-Label Classification 100%
• Random Decision Forest 50%
• Deep Neural Network 50%
• Multilayer Perceptron 50%
• Superior Performance 25%
• Model Accuracy 25%
• Support Vector Machine 25%
• Deep Learning Model 25%
• Classification Task 25%
• Classification Problem 25%
• Decision Tree 25%
• Machine Learning Model 25%