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README.md

@@ -1,87 +1,91 @@
----
-language: en
-license: mit
-library_name: scikit-learn
-tags:
-- tabular-classification
-- healthcare
-- clinical-prediction
-- risk-stratification
-- explainable-ai
-metrics:
-- roc_auc
-- accuracy
-- recall
-- f1
-model-index:
-- name: heart-attack-risk-classifier
-  results:
-  - task:
-      type: tabular-classification
-    metrics:
-    - type: roc_auc
-      value: 0.912
-    - type: recall
-      value: 0.923
-    - type: f1
-      value: 0.884
----
-
-# ❤️ Clinical Heart Attack Risk Prediction Model
-
-## 📌 Model Overview
-This calibrated tabular gradient boosting classifier predicts acute myocardial infarction (heart attack) probability from non-invasive clinical intake markers. 
-
-The model is tuned for clinical triage sensitivity, achieving **>92% Recall / Sensitivity** to minimize life-threatening false negatives.
-
----
-
-## 📋 Features Required for Inference
-1. **`age`:** Patient age (years).
-2. **`sex`:** Biological sex (1 = male, 0 = female).
-3. **`cp`:** Chest Pain type (0: Typical Angina, 1: Atypical Angina, 2: Non-Anginal, 3: Asymptomatic).
-4. **`trtbps`:** Resting blood pressure (mm Hg).
-5. **`chol`:** Serum cholesterol (mg/dL).
-6. **`fbs`:** Fasting blood sugar > 120 mg/dL (1 = true, 0 = false).
-7. **`restecg`:** Resting electrocardiographic results (0, 1, 2).
-8. **`thalachh`:** Maximum heart rate achieved during stress test.
-9. **`exng`:** Exercise-induced angina (1 = yes, 0 = no).
-10. **`oldpeak`:** ST depression induced by exercise relative to rest.
-11. **`slp`:** Slope of peak exercise ST segment (0, 1, 2).
-12. **`caa`:** Number of major vessels (0-3) colored by fluoroscopy.
-13. **`thall`:** Thalassemia rate (0-3).
-
----
-
-## 🚀 Quick Start with Python
-
-```python
-import joblib
-import pandas as pd
-from huggingface_hub import hf_hub_download
-
-# Download model from Hugging Face Hub
-model_path = hf_hub_download(repo_id="Jayan12321/heart-attack-risk-classifier", filename="model.joblib")
-model = joblib.load(model_path)
-
-# Sample Patient Vector (High Risk Profile)
-sample_patient = pd.DataFrame([{
-    'age': 63, 'sex': 1, 'cp': 3, 'trtbps': 145, 'chol': 233, 'fbs': 1,
-    'restecg': 0, 'thalachh': 150, 'exng': 0, 'oldpeak': 2.3, 'slp': 0,
-    'caa': 0, 'thall': 1
-}])
-
-prob = model.predict_proba(sample_patient)[0, 1]
-print(f"Predicted Cardiac Event Probability: {prob * 100:.1f}%")
-```
-
----
-
-## 🔬 Benchmark Performance (5-Fold Stratified CV)
-* **ROC-AUC:** `0.912`
-* **Sensitivity / Recall:** `0.923`
-* **Precision:** `0.851`
-* **F1-Score:** `0.884`
-
----
-*Authored and published by [Jayan Gupta](https://huggingface.co/Jayan12321) • Data Scientist & AI Engineer*
+---
+language: en
+license: mit
+library_name: scikit-learn
+tags:
+- tabular-classification
+- healthcare
+- clinical-prediction
+- risk-stratification
+- explainable-ai
+metrics:
+- roc_auc
+- accuracy
+- recall
+- f1
+model-index:
+- name: heart-attack-risk-classifier
+  results:
+  - task:
+      type: tabular-classification
+    metrics:
+    - type: roc_auc
+      value: 0.912
+    - type: recall
+      value: 0.923
+    - type: f1
+      value: 0.884
+---
+
+# Clinical Heart Attack Risk Prediction Model 🫀
+
+A calibrated tabular gradient boosting classifier trained to predict acute myocardial infarction risk from non-invasive clinical intake markers.
+
+- **Associated Space**: [Clinical Heart Attack Risk Triage Calculator](https://huggingface.co/spaces/Jayan12321/clinical-heart-risk-calculator)
+- **Author**: [Jayan Gupta](https://huggingface.co/Jayan12321)
+
+---
+
+## 🎯 Clinical Sensitivity & Objective
+In medical triage modeling, false negatives are far more critical than false positives. This classifier is tuned for high sensitivity, achieving **>92% Recall** on cardiac event detection to minimize life-threatening missed diagnoses.
+
+---
+
+## 📊 Features Required for Inference
+
+| Feature | Description | Values / Range |
+|---|---|---|
+| `age` | Patient age | Years |
+| `sex` | Biological sex | `1` = male, `0` = female |
+| `cp` | Chest Pain type | `0`: Typical Angina, `1`: Atypical, `2`: Non-anginal, `3`: Asymptomatic |
+| `trestbps` | Resting Blood Pressure | mm Hg on admission |
+| `chol` | Serum Cholesterol | mg/dl |
+| `fbs` | Fasting Blood Sugar | `1` if > 120 mg/dl, `0` otherwise |
+| `restecg` | Resting ECG results | `0`: Normal, `1`: ST-T wave abnormality, `2`: Left ventricular hypertrophy |
+| `thalach` | Maximum Heart Rate achieved | bpm |
+| `exang` | Exercise Induced Angina | `1` = yes, `0` = no |
+| `oldpeak` | ST depression induced by exercise | Relative to rest (float) |
+| `slope` | Slope of peak exercise ST segment | `0`: Upsloping, `1`: Flat, `2`: Downsloping |
+| `ca` | Number of major vessels colored by flourosopy | `0-3` |
+| `thal` | Thalassemia status | `1`: Normal, `2`: Fixed defect, `3`: Reversible defect |
+
+---
+
+## 💻 Python Quickstart
+
+```python
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Download model from Hugging Face
+model_path = hf_hub_download(
+    repo_id="Jayan12321/heart-attack-risk-classifier",
+    filename="model.joblib"
+)
+
+# Load classifier
+model = joblib.load(model_path)
+
+# Sample patient features
+# [age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]
+patient_features = [[57, 1, 2, 130, 236, 0, 0, 174, 0, 0.0, 1, 1, 2]]
+
+# Predict calibrated risk probability
+risk_prob = model.predict_proba(patient_features)[0, 1]
+print(f"Predicted Cardiac Event Probability: {risk_prob:.2%}")
+```
+
+---
+
+## ⚠️ Educational Disclaimer
+*This model is provided solely for educational and research demonstration purposes. It is not approved as a medical diagnostic device and must not be used for actual patient diagnosis.*