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.

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📦 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)

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📁 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

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🎯 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

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🧪 Example Usage (Python)

from datasets import load_dataset

dataset = load_dataset("your-username/heart-disease-early-risk")

print(dataset)
print(dataset["train"][0])