Integrating Gait and Clinical Data with Explainable Artificial Intelligence for Parkinson's Prediction: The EDAM System

Nicoletta Balletti, Nicoletta Balletti, Emanuela Guglielmi, Gennaro Laudato, Rocco Oliveto, Rocco Oliveto, Jonathan Simeone, Roberto Zinni

2025

Abstract

Several machine learning (ML) approaches have been introduced for gait and posture analysis, recognized as crucial for early diagnosing neurological disorders, particularly Parkinson’s disease. However, these existing methods are often limited by their lack of integration with other clinical biomarkers and their inability to provide transparent, explainable predictions. To overcome these limitations, we introduce EDAM (Explainable Diagnosis Recommender), a system that leverages Explainable Artificial Intelligence (XAI) techniques to deliver both accurate predictions and clear, interpretable explanations of its diagnostic decisions. We evaluate the capabilities of EDAM in two main areas: distinguishing between healthy individuals and those with Parkinson’s disease, and classifying abnormal gait patterns that may indicate early-stage Parkinson’s disease. To ensure a comprehensive evaluation, we constructed one of the largest known dataset by merging and standardizing several existing datasets. This dataset includes 557 features and 7,303 labelled instances, covering a wide range of gait patterns and clinical features. Results show that EDAM achieves high accuracy in both tasks, demonstrating its potential for early detection of neurological disorders.

Paper Citation

in Harvard Style

Balletti N., Guglielmi E., Laudato G., Oliveto R., Simeone J. and Zinni R. (2025). Integrating Gait and Clinical Data with Explainable Artificial Intelligence for Parkinson's Prediction: The EDAM System. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-731-3, SciTePress, pages 129-140. DOI: 10.5220/0013179400003911

in Bibtex Style

@conference{healthinf25,
author={Nicoletta Balletti and Emanuela Guglielmi and Gennaro Laudato and Rocco Oliveto and Jonathan Simeone and Roberto Zinni},
title={Integrating Gait and Clinical Data with Explainable Artificial Intelligence for Parkinson's Prediction: The EDAM System},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2025},
pages={129-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013179400003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Integrating Gait and Clinical Data with Explainable Artificial Intelligence for Parkinson's Prediction: The EDAM System
SN - 978-989-758-731-3
AU - Balletti N.
AU - Guglielmi E.
AU - Laudato G.
AU - Oliveto R.
AU - Simeone J.
AU - Zinni R.
PY - 2025
SP - 129
EP - 140
DO - 10.5220/0013179400003911
PB - SciTePress