Synthetic Migraine Symptoms and Patient Records Dataset

Dataset Features

• Age: Age of the individual experiencing migraines (in years).
• Duration: Duration of migraine episodes (in hours).
• Frequency: Number of migraine episodes per month.
• Location: Location of pain (coded integers representing regions such as frontal, temporal, occipital, etc.).
• Character: Nature of pain (coded integers indicating throbbing, stabbing, dull, etc.).
• Intensity: Pain intensity level (numeric scale).
• Nausea: Presence of nausea during migraine episodes (True/False).
• Vomit: Presence of vomiting (True/False).
• Phonophobia: Sensitivity to sound (True/False).
• Photophobia: Sensitivity to light (True/False).
• Visual: Visual disturbances or aura symptoms (coded values).
• Sensory: Sensory disturbances (coded values).
• Dysphasia: Difficulty with speech during migraine (True/False).
• Dysarthria: Slurred or impaired speech (True/False).
• Vertigo: Experience of dizziness or spinning (True/False).
• Tinnitus: Ringing in the ears (True/False).
• Hypoacusis: Decreased hearing or hearing loss (True/False).
• Diplopia: Double vision (True/False).
• Defect: Visual field defects or blind spots (True/False).
• Ataxia: Impaired coordination (coded values).
• Conscience: Impairment or alteration of consciousness (True/False).
• Paresthesia: Tingling or numbness sensations (True/False).
• DPF: Dysfunctional post-focal signs (True/False).
• Type: Type of migraine (e.g., With Aura, Without Aura, Vestibular Migraine, etc.).

Distribution

Usage

• Neurological Research: Investigate migraine patterns and symptom clusters.
• Machine Learning: Develop models to classify migraine types or predict symptom severity.
• Healthcare Analytics: Analyze correlations between symptoms and demographic factors.
• Education & Training: Provide realistic data for training in neurology, diagnostic modelling, and symptom pattern recognition.

Coverage

License

Who Can Use It

• Neurologists and Medical Researchers: To study migraine symptoms and diagnostics.
• Data Scientists and ML Engineers: For developing classification and prediction algorithms.
• Educators and Students: As a training resource for data analysis in healthcare and neurology contexts.