Synthetic Gastrointestinal Disease Patient Records Dataset

Dataset Features

• Age: Age of the patient in years.
• Gender: Biological sex of the patient (M/F).
• BMI: Body Mass Index.
• Body_Weight: Patient's weight in kilograms.
• Obesity_Status: Categorized as Normal, Overweight, or Obese based on BMI.
• Ethnicity: Ethnic background (e.g., White, Hispanic, Asian, etc.).
• Family_History: Indicates presence of family history of GI conditions (Yes/No).
• Genetic_Markers: Count of relevant genetic risk markers detected.
• Microbiome_Index: Numerical score representing gut microbiota diversity or imbalance.
• Autoimmune_Disorders: Presence of autoimmune conditions (Yes/No).
• H_Pylori_Status: Helicobacter pylori infection status (Yes/No).
• Fecal_Calprotectin: Inflammatory marker measured in stool (numeric count).
• Occult_Blood_Test: Result of hidden blood detection in stool (Positive/Negative).
• CRP_ESR: Combined C-Reactive Protein / Erythrocyte Sedimentation Rate value, an inflammation marker.
• Endoscopy_Result / Colonoscopy_Result / Stool_Culture: Clinical test results (e.g., Normal, Abnormal).
• Diet_Type: Type of diet followed (e.g., Vegetarian, Western, etc.).
• Food_Intolerance: Reported intolerances (Yes/No).
• Smoking_Status / Alcohol_Use / Physical_Activity: Lifestyle habits.
• Stress_Level: Reported level of psychological stress (Low/Moderate/High). Note: Some entries missing.
• GI Symptoms: Includes:
  • Abdominal_Pain, Bloating, Diarrhea, Constipation
  • Rectal_Bleeding, Appetite_Loss, Weight_Loss
• Bowel_Habits: Overall pattern (e.g., Normal, Frequent, Irregular).
• Bowel_Movement_Frequency: Number of bowel movements per week.
• Medication Use: Includes:
  • NSAID_Use (e.g., ibuprofen), Antibiotic_Use, PPI_Use (proton-pump inhibitors), Medications (Yes/No)
• Disease_Class: Primary GI-related condition diagnosed (e.g., Blood in stool, Nausea or vomiting, Abdominal cramps or pain, Unexplained weight loss).

Distribution

Usage

• Disease Classification: Predict GI disease categories using symptoms and clinical test results.
• Feature Importance Analysis: Understand contributing factors in diagnosis.
• Pattern Mining: Detect associations among lifestyle, symptoms, and microbiome/genetic indicators.
• Model Training: Useful for supervised learning (e.g., random forest, XGBoost) or unsupervised clustering.

Coverage

License

Who Can Use It

• Medical Researchers and GI Specialists: For testing diagnostic hypotheses and exploring symptom clusters.
• Data Scientists and ML Engineers: For building diagnostic classifiers or recommender systems.
• Educators and Students: For practical exercises in predictive modeling and health analytics.