About Dataset

📌 Overview

This dataset is curated to support research in stroke risk prediction, enabling the development of models that estimate:

• Binary Classification: Whether a person is at risk of stroke.
• Regression Analysis: The percentage likelihood of stroke occurrence.

It is designed for use in machine learning and deep learning applications in medical AI and predictive healthcare. The dataset is balanced, with 50% of records for individuals at risk and 50% not at risk.

📜 Dataset Generation Process

The dataset was created through a combination of medical literature review, expert consultation, and statistical modeling. Feature distributions and relationships reflect real-world clinical patterns.

📖 Medical References & Sources

The dataset is grounded in established risk factors from trusted medical sources, including:

• American Stroke Association (ASA): Guidelines on stroke risk and early symptoms.
• Mayo Clinic & Cleveland Clinic: Literature on cardiovascular and stroke risk.
• Harrison’s Principles of Internal Medicine (20th Ed.)
• Stroke Prevention, Treatment, and Rehabilitation (Oxford University Press, 2021)
• The Stroke Book (Cambridge Medicine, 2nd Ed.)
• World Health Organization (WHO) reports on stroke risk and prevention

🔬 Features of the Dataset

1️⃣ Symptoms (Primary Predictors)

All features are binary (1 = present, 0 = absent):

• Chest Pain
• Shortness of Breath
• Irregular Heartbeat
• Fatigue & Weakness
• Dizziness
• Swelling (Edema)
• Pain in Neck/Jaw/Shoulder/Back
• Excessive Sweating
• Persistent Cough
• Nausea/Vomiting
• High Blood Pressure
• Chest Discomfort (Activity)
• Cold Hands/Feet
• Snoring/Sleep Apnea
• Anxiety/Feeling of Doom

2️⃣ Target Variables (Predicted Outcomes)

• At Risk (Binary): 1 if the person is at risk of stroke, 0 otherwise
• Stroke Risk (%): Estimated probability of stroke (0–100)

3️⃣ Demographic Feature

• Age: Stroke risk increases significantly with age

⚡ Why This Dataset is Accurate and Useful?

✅ Balanced Data Distribution:

• 50% at risk, 50% not at risk
• Prevents model bias toward any class

✅ Medically-Inspired Feature Engineering:

• Features validated via clinical guidelines and expert opinion
• Age is a weighted predictor
• Symptom severity is implicitly encoded

✅ Diverse Risk Factors Included:

• Cardiovascular: chest pain, high BP, heartbeat irregularity
• Neurological: dizziness, fatigue, anxiety
• Sleep-related: snoring, sleep apnea

✅ Scalable and ML-Ready:

• Supports classification and regression
• Works with ML (XGBoost, SVM, RF) and DL frameworks (PyTorch, TensorFlow)
• Suitable for Explainable AI (XAI)

📂 Dataset Usage & Applications

• ✅ Predictive Analytics: Early detection and prevention of stroke
• ✅ Healthcare Chatbots: Real-time triage and risk advice
• ✅ Medical Research: Studying patterns in stroke risk
• ✅ Explainable AI: Understanding how models assess stroke likelihood