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| title: HeartGuard AI | |
| emoji: 📚 | |
| colorFrom: pink | |
| colorTo: yellow | |
| sdk: streamlit | |
| sdk_version: 1.44.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: 'Predict heart disease risk in seconds using clinical data ' | |
| # ❤️ HeartGuard AI - Cardiovascular Risk Prediction System | |
|  | |
| **Developed by Musabbir KM** | |
| ## 🚀 Overview | |
| An end-to-end machine learning system that predicts heart disease risk using clinical features, featuring: | |
| - **XGBoost Classifier** with automated threshold optimization | |
| - **Streamlit Web Application** for interactive predictions | |
| - **Comprehensive Model Evaluation** (ROC AUC: 0.909) | |
| - **Production-Ready Pipeline** with feature engineering | |
| ## 🌟 Key Features | |
| | Feature | Description | | |
| |---------|-------------| | |
| | **Clinical Risk Assessment** | Classifies patients into High/Medium/Low risk categories | | |
| | **Batch Processing** | Handles CSV uploads for multiple predictions | | |
| | **Interactive Interface** | User-friendly Streamlit dashboard | | |
| | **Model Explainability** | Detailed feature importance analysis | | |
| | **Medical Recommendations** | Actionable insights based on risk level | | |
| ## 📊 Dataset Information | |
| **Source:** [UCI Heart Disease Dataset](https://archive.ics.uci.edu/dataset/45/heart+disease) | |
| **Samples:** 303 patients (Cleaned: 297) | |
| **Features:** 13 clinical + 3 engineered features | |
| **Attributes**: | |
| - Demographic: Age, Sex | |
| - Medical: | |
| - cp (Chest Pain Type) | |
| - trestbps (Resting Blood Pressure) | |
| - chol (Serum Cholesterol) | |
| - fbs (Fasting Blood Sugar) | |
| - restecg (Resting ECG) | |
| - thalach (Maximum Heart Rate) | |
| - exang (Exercise Induced Angina) | |
| - oldpeak (ST Depression) | |
| - slope (ST Segment Slope) | |
| - ca (Major Vessels) | |
| - thal (Thalassemia) | |
| ## 📊 Feature Description | |
| -age Age in years | |
| sex Gender (1 = male, 0 = female) | |
| cp Chest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic) | |
| trestbps Resting blood pressure (in mm Hg) | |
| chol Serum cholesterol level (in mg/dl) | |
| fbs Fasting blood sugar > 120 mg/dl (1 = true, 0 = false) | |
| restecg Resting electrocardiographic results (0, 1, or 2) | |
| thalach Maximum heart rate achieved | |
| exang Exercise-induced angina (1 = yes, 0 = no) | |
| oldpeak ST depression induced by exercise relative to rest | |
| slope Slope of the peak exercise ST segment (1, 2, 3) | |
| ca Number of major vessels (0–3) colored by fluoroscopy | |
| thal Thalassemia (3 = normal, 6 = fixed defect, 7 = reversible defect) | |
| ## 📊 Performance Metrics | |
| | Metric | Score | | |
| |---------------|--------| | |
| | Accuracy | 85.2% | | |
| | Precision | 84.7% | | |
| | Recall | 87.5% | | |
| | F1-Score | 85.2% | | |
| (Validation set performance) | |
| # 🏆 Model Performance | |
| ## === Optimized Performance Metrics === | |
| - **Optimal Threshold:** `0.327` | |
| - **Evaluation on Test Set:** `n = 46` | |
| ### 📊 Classification Report | |
| | Class | Precision | Recall | F1-Score | Support | | |
| |----------------|-----------|--------|----------|---------| | |
| | Healthy | 0.95 | 0.76 | 0.84 | 25 | | |
| | Heart Disease | 0.77 | 0.95 | 0.85 | 21 | | |
| ### ✅ Overall Metrics | |
| - **Accuracy:** `0.85` | |
| - **Macro Average:** | |
| - Precision: `0.86` | |
| - Recall: `0.86` | |
| - F1-Score: `0.85` | |
| - **Weighted Average:** | |
| - Precision: `0.87` | |
| - Recall: `0.85` | |
| - F1-Score: `0.85` | |
| --- | |
| 📌 This optimized threshold enhances **Heart Disease detection** (high recall) while maintaining high precision for **Healthy** predictions. | |
| ## 🧠 Model & System Info | |
| - **Model Name:** Heart-Guard | |
| - **Version:** 1.1 | |
| - **Classifier:** XGBoost | |
| - **Optimized Threshold:** 0.327 | |
| - **Deployment:** Streamlit App | |
| ## ⚠️ Important Disclaimer | |
| **This is NOT a medical diagnostic device.** By using this model, you agree that: | |
| - It should not replace professional medical advice | |
| - It is not for use in emergency situations | |
| - Treatment decisions should not be based solely on its outputs | |
| - Always consult qualified healthcare professionals | |
| **Dataset Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/45/heart+disease) | |
| ## 🛠️ Installation | |
| 1. Clone repository: | |
| ```bash | |
| git clone https://github.com/musabbirkm/heart-disease-predictor.git | |
| pip install -r requirements.txt | |
| cd heart-disease-predictor | |
| streamlit run app.py | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |