README.md

metadata

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
• Author: Jayan Gupta

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🎯 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.

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📊 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

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💻 Python Quickstart

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%}")

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⚠️ 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.