roronoazoroinsome/heart_disease_monitoring

# 🩺 Heart Disease Early Risk Detection Dataset A clean, structured dataset containing patient vital-sign measurements collected from a hospital setting for early detection of heart-related risks. The dataset supports **multi-class classification** with three risk levels: - **0 — Normal** - **1 — Abnormal** - **2 — Risk** This dataset is suitable for machine learning, statistical modeling, and health analytics research. --- ## 📦 Dataset Summary This dataset includes physiological features such as age, oxygen saturation (SpO2), heart rate (BPM), and body temperature. These attributes are commonly used as early indicators for cardiovascular stress or potential heart disease. - **Features:** 6 - **Target variable:** heart_risk - **Task:** Multi-class Classification - **License:** CC BY 4.0 - **Source:** Hospital-collected physiological data (All PII removed) --- ## 📁 Dataset Structure ### ➤ **Columns Description** | Column | Type | Description | |------------------|---------|-------------| | `age` | int | Age of the patient in years | | `gender` | int | 0 = Male, 1 = Female (or your mapping) | | `spo2` | int | Blood oxygen saturation (%) | | `bpm` | int | Heart rate in beats per minute | | `body_temperature` | float | Body temperature in °C | | `heart_risk` | int | Target label: 0 = Normal, 1 = Abnormal, 2 = Risk | --- ## 🎯 Use Cases - Heart disease risk prediction - Early detection systems - IoT health monitoring models - Medical anomaly detection - Health research and analytics - Educational and academic projects - Multi-class supervised learning --- ## 🧪 Example Usage (Python) ```python from datasets import load_dataset dataset = load_dataset("your-username/heart-disease-early-risk") print(dataset) print(dataset["train"][0])