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Machine LearningCase Study·89% Match

Heart Disease Prediction with Generative AI

Medical data analysis platform combining an ML classification pipeline (app.py + data_pipeline.py + eda.py) with an OpenAI-powered natural-language explanation layer. Patients receive a risk score plus a plain-English breakdown of contributing clinical factors. Fully containerised with Docker Compose and deployed live on Streamlit Cloud.

PythonGenerative AIOpenAIStreamlitMachine LearningDocker
LiveOpenAILLM LayerStreamlitCloud DeployDockerContainerisedNLPExplanations★ Featured
OpenAI
LLM Layer
Streamlit
Cloud Deploy
Docker
Containerised
NLP
Explanations
🔴 The Problem
▸

ML classifiers produce a score but no actionable explanation

▸

Non-technical patients cannot interpret raw probabilities

✅ The Solution
▸

OpenAI GPT layer translates ML risk scores into plain-English clinical narratives

▸

Docker Compose separates ML pipeline from the GenAI explanation service

📈 Impact & Results
▸

Patients receive a risk score plus a natural-language summary of top risk factors

▸

Deployed live on Streamlit Cloud with zero infra management

▸

Fully containerised — reproducible locally with docker-compose up

Full Tech Stack
PythonGenerative AIOpenAIStreamlitMachine LearningDocker

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