How akhil200331/parkinson-disease-prediction-using-xai-and-attention-mechanism works
This repository presents notebook-based Parkinson disease prediction work, with machine-learning models, DNN models, self-attention, and explainability for both ML and DNN models. The README names these capabilities but provides no source excerpts or details about data inputs, execution order, model outputs, or notebook-to-notebook calls. Accordingly, the graph shows the documented capabilities and the explicitly named explainability relationships, without inferring a runtime workflow.
Main entry points: XAI.ipynb ML Explainability · dnn-parkinson_XAI.ipynb DNN Explainability · parkinson.ipynb ML Models Part 1 · dnn-parkinson.ipynb DNN Models
Components 6
Prediction Models
- ML Models Part 1
parkinson.ipynb - ML Models Part 2
final.ipynb - DNN Models
dnn-parkinson.ipynb
Attention
- Self-Attention
Self_attention.ipynb
Explainability
- ML Explainability
XAI.ipynb - DNN Explainability
dnn-parkinson_XAI.ipynb
Connections 2
- ML Explainability → ML Models Part 1: explains ML
- DNN Explainability → DNN Models: explains DNN