Synthetic Panic Attack Dataset

Dataset Features

• ID: Unique identifier for each individual.
• Age: Age of the individual (in years).
• Panic_Attack_Frequency: Number of panic attacks experienced per week.
• Duration_Minutes: Average length of a panic attack episode (in minutes).
• Heart_Rate: Average heart rate recorded during a panic attack (in beats per minute).
• Caffeine_Intake: Daily caffeine intake level.
• Exercise_Frequency: Number of days the individual exercises per week.
• Sleep_Hours: Average number of hours slept per night.
• Alcohol_Consumption: Frequency or level of alcohol consumption.
• Gender: Gender identity of the individual (Female, Male, Non-binary).
• Trigger_Caffeine: Panic attacks triggered by caffeine.
• Trigger_PTSD: Panic attacks triggered by post-traumatic stress disorder.
• Trigger_Phobia: Panic attacks caused by phobic stimuli.
• Trigger_Social_Anxiety: Panic attacks related to social anxiety.
• Trigger_Stress: Panic attacks triggered by stress.
• Trigger_Unknown: No identifiable trigger reported.
• Sweating: Indicates whether sweating occurred during an attack.
• Shortness_of_Breath: Presence of breathing difficulties during an episode.
• Dizziness: Reports of dizziness or lightheadedness during the attack.
• Chest_Pain: Whether chest pain was experienced.
• Trembling: Whether the individual experienced trembling or shaking.
• Medical_History_Anxiety: History of diagnosed anxiety disorder.
• Medical_History_Depression: History of depression.
• Medical_History_PTSD: Previous diagnosis of PTSD.
• Medical_History_Missing: Medical history not disclosed or unavailable.
• Medication: Indicates whether the individual is currently on medication.
• Smoking: Whether the individual is a smoker.
• Therapy: Indicates participation in psychological therapy.

Distribution

Usage

• Behavioural Health Analytics: Analyse how physiological, psychological, and lifestyle variables relate to panic attack severity and frequency.
• Predictive Modelling: Develop machine learning models to predict panic attack risk or treatment outcomes.
• Intervention Strategy Development: Identify trends and triggers to guide hypothetical health interventions.
• Education: Provide students with a structured dataset to learn statistical analysis, data visualisation, and mental health modelling.

Coverage

License

Who Can Use It

• Mental Health Researchers: To explore behavioural and physiological dimensions of panic disorders.
• Data Scientists & Machine Learning Engineers: To build and test predictive models for mental health outcomes.
• Educators and Students: For teaching regression, classification, feature selection, and ethical health data practices.
• Public Health Analysts: To model population-level trends and hypothetical outcomes based on lifestyle and treatment choices.