How rbnarule3007/combining-heart-imaging-with-advanced-ai-for-better-heart-disease-detection works
Heart Disease Prediction is a Streamlit application that accepts an ECG image and displays a predicted class. Its main flow reads the upload, divides it into leads, extracts and combines scaled signals, then analyzes PQRST waves, applies PCA, and predicts a diagnosis. The UI also displays intermediate images and results. The edges follow calls visible in the app and ECG excerpts; the README documents the Streamlit launch command but no additional capabilities.
Main entry points: final_app.py Streamlit workflow · Ecg.py Signal extraction · Scaled_1DLead_1.csv Lead CSV files · pca-final.pkl PCA model
Components 13
User interface
- Streamlit workflow
final_app.py
Signal processing
- Image reader
Ecg.py - Lead segmentation
Ecg.py - Lead preprocessing
Ecg.py - Signal extraction
Ecg.py - Lead CSV files
Scaled_1DLead_1.csv - Signal combination
Ecg.py
Analysis and prediction
- PQRST analysis
Ecg.py - PCA reduction
Ecg.py - PCA model
pca-final.pkl - ECG classifier
Ecg.py - Classifier model
best.pkl
Other components
- ECG uploader
Connections 13
- ECG uploader → Streamlit workflow: uploads image
final_app.py - Streamlit workflow → Image reader: reads upload
final_app.py - Streamlit workflow → Lead segmentation: segments image
final_app.py - Streamlit workflow → Lead preprocessing: preprocesses leads
final_app.py - Streamlit workflow → Signal extraction: extracts signals
final_app.py - Signal extraction → Lead CSV files: writes signals
Ecg.py - Streamlit workflow → Signal combination: combines signals
final_app.py - Signal combination → Lead CSV files: reads signals
Ecg.py - Streamlit workflow → PQRST analysis: analyzes waves
final_app.py - Streamlit workflow → PCA reduction: reduces signals
final_app.py - PCA reduction → PCA model: loads model
Ecg.py - Streamlit workflow → ECG classifier: predicts class
final_app.py - ECG classifier → Classifier model: loads model
Ecg.py