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Machine LearningCase Study·91% Match

Heart Disease Prediction System

Production ML system with MLflow experiment tracking, DagsHub integration, Streamlit UI deployed on DigitalOcean with Docker. High-accuracy ensemble classification.

GitHub ↗
PythonMLflowDagsHubStreamlitDockerDigitalOceanSQLite3
Research92.3%Accuracy8Classifiers47ExperimentsSHAPExplainability
92.3%
Accuracy
8
Classifiers
47
Experiments
SHAP
Explainability
🔴 The Problem
▸

Clinicians needed fast, explainable cardiac risk stratification

▸

Black-box predictions unacceptable clinically

✅ The Solution
▸

8 classifiers via MLflow tracking; XGBoost wins at 92.3%

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SHAP values provide per-patient feature attribution

📈 Impact & Results
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92.3% accuracy on UCI Heart Disease dataset

▸

SHAP shows top 5 risk factors per patient

▸

47 MLflow experiments — fully reproducible

Full Tech Stack
PythonMLflowDagsHubStreamlitDockerDigitalOceanSQLite3

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