Attention-Based Deep Learning for Early Parkinson's Disease Detection with Tabular Biomedical Data

TL;DR

This paper demonstrates that attention-based deep learning models, particularly SAINT, significantly improve early Parkinson's disease detection accuracy using tabular biomedical data, outperforming traditional models.

Contribution

The study introduces the application of attention-based deep learning models, especially SAINT, for early PD detection, showing superior performance over existing methods on biomedical data.

Findings

SAINT achieved a weighted precision of 0.98 and recall of 0.97.

SAINT outperformed baseline models across multiple metrics.

Attention mechanisms effectively model feature interactions for clinical prediction.

Abstract

Early and accurate detection of Parkinson's disease (PD) remains a critical challenge in medical diagnostics due to the subtlety of early-stage symptoms and the complex, non-linear relationships inherent in biomedical data. Traditional machine learning (ML) models, though widely applied to PD detection, often rely on extensive feature engineering and struggle to capture complex feature interactions. This study investigates the effectiveness of attention-based deep learning models for early PD detection using tabular biomedical data. We present a comparative evaluation of four classification models: Multi-Layer Perceptron (MLP), Gradient Boosting, TabNet, and SAINT, using a benchmark dataset from the UCI Machine Learning Repository consisting of biomedical voice measurements from PD patients and healthy controls. Experimental results show that SAINT consistently outperformed all baseline models across multiple evaluation metrics, achieving a weighted precision of 0.98, weighted recall of 0.97, weighted F1-score of 0.97, a Matthews Correlation Coefficient (MCC) of 0.9990, and the highest Area Under the ROC Curve (AUC-ROC). TabNet and MLP demonstrated competitive performance, while Gradient Boosting yielded the lowest overall scores. The superior performance of SAINT is attributed to its dual attention mechanism, which effectively models feature interactions within and across samples. These findings demonstrate the diagnostic potential of attention-based deep learning architectures for early Parkinson's disease detection and highlight the importance of dynamic feature representation in clinical prediction tasks.