Synthetic Colorectal Cancer Global Dataset

Dataset Features

• Patient_ID: Unique identifier for each patient.
• Country: Patient's country of residence.
• Age: Age at diagnosis (in years).
• Gender: Biological sex of the patient (Male/Female/Other).
• Cancer_Stage: Stage of colorectal cancer at diagnosis (e.g., Stage I–IV).
• Tumor_Size_mm: Size of the tumor in millimeters.
• Family_History: Presence of colorectal cancer in family history (True/False).
• Smoking_History: Smoking behavior or history (e.g., Current, Former, Never).
• Alcohol_Consumption: Level of alcohol consumption (e.g., High, Moderate, None).
• Obesity_BMI: BMI classification related to obesity.
• Diet_Risk: Diet-related cancer risk (e.g., High Fat, Low Fiber).
• Physical_Activity: Level of physical activity (e.g., Sedentary, Active).
• Diabetes: Diabetes diagnosis (True/False).
• Inflammatory_Bowel_Disease: Presence of IBD (True/False).
• Genetic_Mutation: Genetic mutations relevant to colorectal cancer (e.g., APC, KRAS).
• Screening_History: History of cancer screenings (True/False).
• Early_Detection: Whether cancer was detected early (True/False).
• Treatment_Type: Primary treatment type (e.g., Surgery, Chemotherapy, Radiation).
• Survival_5_years: 5-year survival status (True/False).
• Mortality: Mortality outcome (Alive/Deceased).
• Healthcare_Costs: Estimated treatment costs (in USD).
• Incidence_Rate_per_100K: Country-level incidence rate per 100,000 people.
• Mortality_Rate_per_100K: Country-level mortality rate per 100,000 people.
• Urban_or_Rural: Patient's living area (Urban/Rural).
• Economic_Classification: Country's economic level (e.g., Low, Middle, High income).
• Healthcare_Access: Access level to healthcare services (e.g., Good, Limited).
• Insurance_Status: Insurance coverage status (Insured/Uninsured).
• Survival_Prediction: Model-derived survival prediction (probability or binary).

Distribution

Usage

• Global Cancer Research: Analyze how clinical, lifestyle, and socioeconomic factors affect colorectal cancer outcomes worldwide.
• Predictive Modeling: Develop models to estimate survival probability or treatment outcomes.
• Healthcare Policy Analysis: Study disparities in healthcare access and outcomes across countries.
• Educational Use: Support training in epidemiology, oncology, public health, and machine learning.

Coverage

License

Who Can Use It

• Epidemiologists and Medical Researchers: To explore global patterns in colorectal cancer.
• Public Health Experts and Policymakers: For assessing equity in healthcare access and cancer outcomes.
• Data Scientists and Educators: As a rich dataset for teaching data analysis, classification, regression, and health informatics.