Synthetic Hepatitis C Patient Records

This synthetic Hepatitis C Dataset is designed for educational and research purposes in the fields of data science, healthcare analytics, and disease prediction. It contains key health indicators such as liver function tests, age, sex, and other medical markers that can be used to analyze liver diseases, specifically Hepatitis C and related conditions. The dataset is useful for building predictive models, assessing health risks, and studying disease patterns related to liver health.

Dataset Features: Category: The diagnosis or health status of the individual, represented by categorical values ('0=Blood Donor','0s=suspect Blood Donor', etc) Age (yrs): Age of the individual (in years). Sex: Gender of the individual. ALB (g/dL): Albumin level in grams per deciliter (g/dL), an important protein marker for liver function. AST (U/L): Aspartate aminotransferase level in units per liter (U/L), a liver enzyme that indicates liver damage. BIL (mg/dL): Bilirubin level in milligrams per deciliter (mg/dL), which can indicate liver or red blood cell issues. CHE (U/L): Cholinesterase enzyme level in units per liter (U/L), a marker of liver function. CREA (mg/dL): Creatinine level in milligrams per deciliter (mg/dL), a marker of kidney function. GGT (U/L): Gamma-glutamyl transferase level in units per liter (U/L), an enzyme related to liver disease and bile duct issues. PROT (g/dL): Total protein level in grams per deciliter (g/dL), reflecting liver function and nutritional status. Usage: This dataset is ideal for a variety of healthcare-related applications:

Disease Diagnosis and Classification: Build machine learning models to predict liver diseases such as Cirrhosis, Hepatitis, and Fibrosis based on the health indicators. Health Risk Analysis: Evaluate the risk factors related to liver diseases, using key metrics like liver enzymes, bilirubin levels, and albumin. Predictive Modeling: Develop predictive models to assess the likelihood of different liver conditions based on the patient's clinical data. Healthcare Research: Conduct studies exploring the relationship between lab markers and liver conditions, or investigate the effectiveness of early diagnosis methods. Public Health Analysis: Use the dataset to examine population-level health trends, and guide public health initiatives focused on liver disease prevention and management. Coverage: This synthetic dataset is anonymized, ensuring privacy and compliance with data protection regulations. It is designed for research and learning purposes, offering a diverse set of individuals and conditions for model building and analysis.

License: CCo (Public Domain)

Who Can Use It: Data Science Learners: Ideal for practicing data preprocessing, classification tasks, and model development. Healthcare Professionals and Researchers: Useful for studying liver diseases and improving diagnostic approaches using lab results and patient data. Medical Analysts: For building models to predict liver diseases, estimate patient risk, and analyze clinical data. Public Health Officials: For understanding trends in liver diseases and planning targeted interventions based on health data.