**These datasets are for educational purposes only. Any misuse for illegal or unethical activities is strictly prohibited. All generated data is fictional and has no real-world validity.
Contains basic details about diabetes patients, including age, gender, and contact details.
| Patient ID | Name | Age | Gender | Contact Number | Diabetes Type | Insurance Provider | Address | Primary Care Physician | |
|---|---|---|---|---|---|---|---|---|---|
| D1001 | Emily Turner | 47 | Female | 555-6789 | emily.turner@example.com | Type 2 | MediCare | 21 Park Lane, Chicago | Dr. Laura Hill |
Includes details about diabetes diagnosis, such as type, date diagnosed, and diagnosis details.
| Patient ID | Diabetes Type | Date Diagnosed | Diagnosis Details |
|---|---|---|---|
| D1001 | Type 2 | 2018-05-12 | Elevated HbA1c and fasting glucose levels |
Captures information about treatment methods, medications, and dosages.
| Patient ID | Treatment | Medication Used | Start Date | End Date | Dosage |
|---|---|---|---|---|---|
| D1001 | Oral Medication | Metformin | 2018-06-01 | Ongoing | 500 mg twice daily |
Contains various lab test results relevant to diabetes management.
| Patient ID | Test Type | Result | Test Date | Blood Glucose Level (mg/dL) | HbA1c (%) | Cholesterol (mg/dL) |
|---|---|---|---|---|---|---|
| D1001 | Blood Test | Above Normal | 2024-03-15 | 160 | 7.2 | 205 |
Tracks insulin usage, dosage, and injection frequency of patients.
| Patient ID | Insulin Type | Start Date | Dosage (units) | Injection Frequency |
|---|---|---|---|---|
| D1001 | Basaglar | 2022-09-10 | 12 | Twice Daily |
Records any complications arising from diabetes, their severity, and treatment plans.
| Patient ID | Complication Type | Diagnosis Date | Severity | Treatment Plan |
|---|---|---|---|---|
| D1001 | Neuropathy | 2023-01-20 | Moderate | Gabapentin + Physical Therapy |
Contains scheduled follow-up appointments for diabetes patients.
| Patient ID | Appointment Date | Specialist | Appointment Type | Next Appointment |
|---|---|---|---|---|
| D1001 | 2025-04-18 | Endocrinologist | Routine Diabetes Review | 2025-10-18 |
Lists emergency contacts for diabetes patients, including their relationship and phone number.
| Patient ID | Emergency Contact Name | Relationship | Contact Number | Address |
|---|---|---|---|---|
| D1001 | Michael Turner | Husband | 555-3344 | 21 Park Lane, Chicago |
A diabetes dataset is a collection of structured data related to patients with diabetes, including demographic, lifestyle, clinical, and lab information. These datasets help in analyzing disease patterns, predicting risk, and exploring correlations for research or educational purposes.
You can learn to:
No. The datasets are synthetically generated to reflect real-world clinical scenarios while ensuring privacy and safety. They are designed purely for educational and analytical practice.
Yes. Many datasets include time-stamped lab results, treatment dates, and follow-up appointments, allowing you to track patient progress longitudinally and study how interventions influence health outcomes over time.
Some datasets include comorbidity fields like hypertension, cardiovascular disease, or obesity. These allow users to study how diabetes interacts with other health conditions and affects overall treatment plans.
Absolutely. You can create rule-based scenarios (e.g., when to escalate insulin dosage) or build decision trees that mimic real-world clinical guidelines to explore how different inputs lead to treatment changes.
Yes. Just like real-world medical data, these datasets may include missing values, outliers, or inconsistent coding. They offer a great opportunity to practice data cleaning and preprocessing techniques essential for healthcare analytics.
The datasets are generated using statistical models and medical knowledge to simulate realistic distributions of key variables (e.g., glucose levels, BMI, age) while ensuring no real patient data is used or exposed.
All datasets are completely synthetic, meaning they do not contain any personally identifiable information (PII). They're safe for open use in educational, research, and training environments.
Yes. You can combine diabetes datasets with other synthetic healthcare data (e.g., hospitalization records or insurance claims) to build multi-source analyses or simulate full patient journeys.
Useful visualizations include:
Diabetes datasets combine continuous variables (like blood sugar) with categorical attributes (like lifestyle or medication), making them well-suited for both exploratory data analysis and machine learning. The chronic nature of the disease also supports longitudinal analysis.
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