Healthcare Dataset

Objective:

The dataset addresses the need for accessible healthcare data that complies with privacy regulations. With synthetic records, users can simulate predictive modeling, enhance their data manipulation skills, and explore healthcare trends.

Dataset Overview:

The dataset contains various fields relevant to patient admissions and healthcare delivery. Below is an outline of the included columns:

• Patient Name: Identifies patients with generated names.
• Age: The patient’s age at admission.
• Gender: Gender classification (Male/Female).
• Blood Type: Blood group, e.g., A+, O-.
• Medical Condition: Primary diagnosis, e.g., Diabetes, Hypertension.
• Date of Admission: Entry date into the facility.
• Doctor: Assigned medical professional during the patient’s stay.
• Hospital: The facility name where the treatment occurred.
• Insurance Provider: Insurer, including options like Aetna, Medicare.
• Billing Amount: Total costs incurred during treatment.
• Room Number: Accommodation within the facility.
• Admission Type: Classifies the type of admission (Emergency, Elective).
• Discharge Date: Exit date from the healthcare facility.
• Medication: Drugs administered, e.g., Aspirin, Ibuprofen.

Usage and Application:

This dataset is highly adaptable, supporting various analytical tasks such as:

• Healthcare Predictive Modeling: Train models to predict outcomes, diagnose conditions, or estimate length of stay.
• Data Cleaning & Transformation: A great resource for practicing data preprocessing techniques.
• Visualization & Trend Analysis: Create visual reports for patient data trends, treatment outcomes, or hospital performance.
• Classification Problem: Model classification tasks by predicting test results as “Normal,” “Abnormal,” or “Inconclusive.”