Heart Disease MLOps

A production-ready machine learning pipeline to predict heart disease, built with scikit-learn, FastAPI, MLflow, and Docker.

Dataset

• Source: UCI Heart Disease Dataset
• 4 hospitals: Cleveland, Hungarian, Switzerland, VA Long Beach
• 920 patients total | 13 features | Binary target (0=Healthy, 1=Disease)

Project Structure

heart-disease-mlops/
├── data/ # Raw and cleaned datasets
├── src/
│ ├── preprocess.py # Data cleaning & merging
│ ├── train.py # Model training + MLflow tracking
│ └── api.py # FastAPI prediction API
├── models/ # Saved trained models
├── test_api.py # API tests
├── Dockerfile # Container definition
├── docker-compose.yml # Container orchestration
└── requirements.txt # Dependencies

How to Run

1. Setup

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Preprocess data

python src/preprocess.py

3. Train model

python src/train.py

4. Start API

uvicorn src.api:app --reload

5. Run with Docker

docker-compose up --build

API Endpoints

Method	Endpoint	Description
GET	/	Health check
POST	/predict	Predict heart disease

Example request:

{
  "age": 63.0, "sex": 1.0, "cp": 4.0,
  "trestbps": 145.0, "chol": 233.0, "fbs": 1.0,
  "restecg": 2.0, "thalach": 150.0, "exang": 0.0,
  "oldpeak": 2.3, "slope": 3.0, "ca": 0.0, "thal": 6.0
}

Example response:

{
  "prediction": 1,
  "diagnosis": "Heart Disease",
  "probability": 0.821
}

Results

Model	Accuracy	ROC-AUC
Logistic Regression	~83%	~0.90
Random Forest ✅	~83%	0.919

Tech Stack

• ML: scikit-learn
• API: FastAPI + Uvicorn
• Tracking: MLflow
• Container: Docker
• Language: Python 3.11