Using Base-ml to Learn Classification of Common Vestibular Disorders on DizzyReg Registry Data

Keyphrases

• Registry Data 100%
• Vestibular Disorders 100%
• Multivariable 100%
• Classification Accuracy 60%
• Clinical Task 60%
• Functional Dizziness 60%
• Deep Learning Model 40%
• Random Forest 40%
• Logistic Regression 40%
• Classification Results 20%
• Support Vector Machine 20%
• Machine Learning Techniques 20%
• Traditional Machine Learning 20%
• Machine Learning 20%
• Classification Metrics 20%
• Statistical Analysis 20%
• Machine Learning Algorithms 20%
• Clinically Significant 20%
• Input Distribution 20%
• Hyperparameter Optimization 20%
• Explainable AI 20%
• Nave Bayes 20%
• Multiple Classifier System 20%
• Patient Data 20%
• Benign Paroxysmal Positional Vertigo 20%
• Patient Registry 20%
• Regression Forest 20%
• Bilateral Vestibular Failure 20%
• Machine Learning Software 20%
• Vestibular 20%
• Vestibular Paroxysmia 20%
• Neurotology 20%
• Meniere's Disease 20%
• De-implementation 20%
• Vestibular Migraine 20%
• Graph Deep Learning 20%
• Machine Learning pipelines 20%
• Task Classification 20%
• Artificial Neural Network Model 20%
• Stratified Cross-validation 20%
• Clinical Patients 20%
• Methodological Basis 20%
• Clinical Decision Support 20%
• Graph-based Deep Learning 20%
• Open-source Tools 20%
• Implementation Effort 20%
• Machine Learning Performance 20%
• Clinical Intuition 20%
• Data Loading 20%
• K-nearest 20%

Neuroscience

• Vestibular Disorder 100%
• Support Vector Machine 50%
• Machine Learning Algorithm 50%
• Benign Paroxysmal Positional Vertigo 50%
• Migraine 50%
• Artificial Neural Network 50%