Synthetic Kidney Disease Patient Records Dataset

Dataset Features

• patient_id: Unique identifier for each individual in the dataset.
• age: Age of the individual.
• gender: Gender of the individual (e.g., Male, Female).
• ethnicity: Ethnic background of the individual (e.g., Caucasian, Asian).
• socioeconomic_status: Socioeconomic status of the individual (e.g., High, Middle, Low).
• education_level: Highest level of education attained by the individual (e.g., High School, Bachelor's).
• bmi: Body Mass Index (BMI) of the individual.
• smoking: Whether the individual is a smoker ("Yes" or "No").
• alcohol_consumption: Frequency of alcohol consumption (e.g., daily, weekly).
• physical_activity: Level of physical activity (numeric value).
• diet_quality: Quality of the individual's diet (numeric value).
• sleep_quality: Quality of the individual's sleep (numeric value).
• family_history_kidney_disease: Whether the individual has a family history of kidney disease.
• family_history_hypertension: Whether the individual has a family history of hypertension.
• family_history_diabetes: Whether the individual has a family history of diabetes.
• previous_acute_kidney_injury: Whether the individual has a history of acute kidney injury.
• urinary_tract_infections: Whether the individual has a history of urinary tract infections.
• systolic_bp: Systolic blood pressure value.
• diastolic_bp: Diastolic blood pressure value.
• fasting_blood_sugar: Fasting blood sugar level.
• hb_a1c: Hemoglobin A1c level.
• serum_creatinine: Serum creatinine level, an indicator of kidney function.
• bunlevels: Blood urea nitrogen (BUN) levels.
• gfr: Glomerular filtration rate, an indicator of kidney function.
• protein_in_urine: Amount of protein present in the urine.
• acr: Albumin-to-creatinine ratio in urine.
• serum_electrolytes_sodium: Sodium levels in serum electrolytes.
• serum_electrolytes_potassium: Potassium levels in serum electrolytes.
• serum_electrolytes_calcium: Calcium levels in serum electrolytes.
• serum_electrolytes_phosphorus: Phosphorus levels in serum electrolytes.
• hemoglobin_levels: Hemoglobin levels.
• cholesterol_total: Total cholesterol level.
• cholesterol_ldl: Low-density lipoprotein (LDL) cholesterol level.
• cholesterol_hdl: High-density lipoprotein (HDL) cholesterol level.
• cholesterol_triglycerides: Triglycerides level in the blood.
• aceinhibitors: Whether the individual is on ACE inhibitors.
• diuretics: Whether the individual is on diuretics.
• nsaids_use: Whether the individual uses NSAIDs.
• statins: Whether the individual is on statins.
• antidiabetic_medications: Whether the individual is on antidiabetic medications.
• edema: Whether the individual experiences edema (fluid retention).
• fatigue_levels: Level of fatigue experienced by the individual.
• nausea_vomiting: Whether the individual experiences nausea or vomiting.
• muscle_cramps: Whether the individual experiences muscle cramps.
• itching: Whether the individual experiences itching.
• quality_of_life_score: A score indicating the individual's overall quality of life.
• heavy_metals_exposure: Whether the individual has been exposed to heavy metals.
• occupational_exposure_chemicals: Whether the individual has been exposed to chemicals at work.
• water_quality: Quality of the water consumed by the individual.
• medical_checkups_frequency: Frequency of medical checkups attended by the individual.
• medication_adherence: Adherence to prescribed medications.
• health_literacy: The individual’s understanding of health information.
• diagnosis: The final diagnosis of the individual, indicating whether they have kidney disease or not.

Distribution

Usage

• Kidney Disease Prediction: Build machine learning models to predict the likelihood of kidney disease based on contributing factors.
• Risk Factor Analysis: Analyze which factors (e.g., medical conditions, lifestyle choices) are associated with higher risks of developing kidney disease.
• Kidney Function Analysis: Study the relationship between kidney function indicators (e.g., serum creatinine, GFR) and other health metrics.
• Personalized Care Planning: Use insights from the data to guide healthcare professionals in developing tailored care plans for kidney disease patients.
• Public Health Research: Investigate trends and correlations in kidney disease prevalence across different demographics and lifestyle factors.

License

Who Can Use It

• Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks.
• Healthcare Researchers: To explore the relationships between lifestyle factors, medical conditions, and kidney disease.
• Public Health Analysts: To analyze kidney disease prevalence and risk factors in different populations.
• Healthcare Professionals: For guiding diagnosis and treatment plans based on data-driven insights.
• Policy Makers: To make informed decisions related to kidney disease prevention and care.