# keysarenull/heart-disease-predictor architecture

> Architecture diagram of the GitHub repository keysarenull/heart-disease-predictor, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-29). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/keysarenull/heart-disease-predictor
- Repository: https://github.com/keysarenull/heart-disease-predictor

## Overview

This is a small Streamlit health-risk prediction app. A user enters patient features, the app encodes them in the model’s expected order, and the loaded classifier produces a risk label and confidence estimate. The sampled app source supports the form, model-loading, inference, and display flow; the README provides no additional product capabilities.

Main entry points: [`app.py`](https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py) (Risk inference), [`random_forest_heart_disease_model.joblib`](https://github.com/keysarenull/heart-disease-predictor/blob/main/random_forest_heart_disease_model.joblib) (Trained classifier)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_interface["User interface"]
  node_form["Feature form<br/>[app.py]"]
  node_encode["Input encoding<br/>[app.py]"]
  node_result["Risk and confidence<br/>[app.py]"]
end

subgraph group_prediction["Prediction"]
  node_load["Model loading<br/>[app.py]"]
  node_artifact["Trained classifier"]
  node_inference["Risk inference<br/>[app.py]"]
end

node_user(("Patient / user"))

node_user -->|"enters features"| node_form
node_form -->|"collects values"| node_encode
node_load -->|"loads model"| node_artifact
node_encode -->|"provides array"| node_inference
node_artifact -->|"supplies classifier"| node_inference
node_inference -->|"returns prediction"| node_result
node_user -->|"requests prediction"| node_inference

click node_form "https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py"
click node_encode "https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py"
click node_load "https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py"
click node_artifact "https://github.com/keysarenull/heart-disease-predictor/blob/main/random_forest_heart_disease_model.joblib"
click node_inference "https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py"
click node_result "https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_form,node_encode,node_result,node_user toneBlue
class node_load,node_artifact,node_inference toneAmber
```

## Components

### User interface

- [Feature form](https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py): `app.py`
- [Input encoding](https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py): `app.py`
- [Risk and confidence](https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py): `app.py`

### Prediction

- [Model loading](https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py): `app.py`
- [Trained classifier](https://github.com/keysarenull/heart-disease-predictor/blob/main/random_forest_heart_disease_model.joblib): `random_forest_heart_disease_model.joblib`
- [Risk inference](https://github.com/keysarenull/heart-disease-predictor/blob/main/app.py): `app.py`

### Other components

- **Patient / user**

## Connections

- Patient / user → Feature form: enters features
- Feature form → Input encoding: collects values
- Model loading → Trained classifier: loads model
- Input encoding → Risk inference: provides array
- Trained classifier → Risk inference: supplies classifier
- Risk inference → Risk and confidence: returns prediction
- Patient / user → Risk inference: requests prediction