Synthetic Hair Loss Patient Records Dataset

Dataset Features

• ID: Unique identifier for each individual in the dataset.
• Genetics: Indicates whether the individual has a genetic predisposition to hair loss
• Hormonal Changes: Reflects the presence of hormonal factors contributing to hair loss
• Medical Conditions: Includes specific medical conditions such as scalp infections, dermatosis, or thyroid problems.
• Medications & Treatments: Specifies medications or treatments linked to hair loss, such as chemotherapy or steroids.
• Nutritional Deficiencies: Highlights deficiencies in essential nutrients, such as biotin, vitamin D, or omega-3 fatty acids.
• Stress: Categorizes the individual’s stress level, which can contribute to hair loss.
• Age: Age of the individual, as hair loss prevalence increases with age.
• Poor Hair Care Habits: Indicates whether poor hair care practices contribute to hair loss
• Environmental Factors: Considers external factors such as pollution or harsh weather conditions
• Smoking: Reflects whether the individual is a smoker
• Weight Loss: Indicates whether recent or significant weight loss is a contributing factor to hair loss
• Hair Loss: Binary classification of whether the individual is experiencing hair loss:
• Yes: Hair loss is present.
• No: No hair loss is observed.

Distribution

Usage

• Hair Loss Prediction: Develop machine learning models to predict hair loss based on contributing factors.
• Risk Analysis: Identify key factors contributing to hair loss and prioritize treatment or preventive measures.
• Predictive Modeling: Build predictive models to explore the interplay of genetics, lifestyle, and medical conditions on hair loss.
• Public Health Research: Study patterns and trends in hair loss across various demographics and environmental conditions.
• Personalized Treatment Plans: Use insights to guide dermatologists and health professionals in tailoring hair loss treatments.

Coverage

License

Who Can Use It

• Data Science Practitioners: This is for practising data preprocessing, classification, and regression tasks.
• Dermatologists and Medical Researchers: To explore relationships between biological, environmental, and lifestyle factors affecting hair loss.
• Public Health Analysts: To analyse hair loss trends and recommend interventions.
• Cosmetic Scientists and Hair Care Professionals: To guide the development of effective hair care products and solutions.
• Policy Makers and Regulators: For data-driven decision-making in public health initiatives related to hair and skin health.