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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Beyond Accuracy: Enhancing Parkinson's Diagnosis with Uncertainty Quantification of Machine Learning Models.

Asif Azad^1,2, Md Saiful Islam^3,1, Ehsan Hoque^3,2

¹Department of Computer Science & Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

Artificial Intelligence in Healthcare : Second International Conference, Aiih 2025, Cambridge, UK, September 8-10, 2025, Proceedings. Part I. International Conference on Artificial Intelligence in Healthcare (2Nd : 2025 : Cambridge, Eng

|December 22, 2025

Summary

This study evaluated uncertainty estimation methods for Parkinson's disease detection. Monte Carlo Dropout and Bayesian Neural Networks improved model reliability, unlike Deep Evidential Classification, enhancing AI safety in medicine.

Keywords:

Bayesian Neural Network Deep Evidential Classification MCDropout Parkinson’s Diagnosis Uncertainty Quantification

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Last Updated: Jan 8, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Area of Science:

Artificial Intelligence in Medicine
Machine Learning for Healthcare
Computational Neuroscience

Background:

Deep learning and machine learning show promise in clinical diagnosis.
Reliability assessment is crucial for implementing AI in medical settings.
Parkinson's disease detection can benefit from improved diagnostic tools.

Purpose of the Study:

To evaluate uncertainty estimation techniques for enhancing the reliability of machine learning models in Parkinson's disease detection.
To compare the performance of Monte Carlo Dropout, Deep Evidential Classification, and Bayesian Neural Networks.
To identify methods that improve diagnostic accuracy and uncertainty assessment for secure AI adoption.

Main Methods:

Assessed three uncertainty estimation techniques: Monte Carlo Dropout, Deep Evidential Classification, and Bayesian Neural Networks.
Utilized three distinct datasets: finger tapping, facial expressions, and vocal patterns.
Evaluated models based on diagnostic accuracy and the quality of uncertainty estimation.

Main Results:

Deep Evidential Classification demonstrated poor performance in both accuracy and uncertainty estimation.
Monte Carlo Dropout and Bayesian Neural Networks showed enhanced dependability and reliability.
Uncertainty estimation successfully identified ambiguous predictions, reducing potential diagnostic errors.

Conclusions:

Monte Carlo Dropout and Bayesian Neural Networks are promising for reliable Parkinson's disease detection.
Uncertainty quantification is vital for the safe and responsible implementation of AI in clinical diagnosis.
Further research into robust uncertainty estimation methods will accelerate AI adoption in healthcare.