NeuroSignal Precision: A Hierarchical Approach for Enhanced Insights in Parkinson’s Disease Classification

Md Abubakkar

Abstract:

Parkinson’s disease (PD) is a progressive neuro-
logical disorder that affects movement, posture, handwriting,
and speech. Parkinson’s disease is challenging to diagnose early
due to subtle symptoms that often go unnoticed, necessitat-
ing reliable and accurate classification models to aid clinical
decision-making. This research introduces a comprehensive
benchmarking of nine unified models, and a unique contribution
of this research is the adaptation of the Tabular Transformer
model for structured medical data, achieving an unprecedented
accuracy of 99.49%, setting a new benchmark for Parkinson’s
disease classification. The proposed approach provides an ad-
vanced, adaptable framework that supports clinicians in making
early, accurate diagnoses, ultimately improving patient care.
In contrast to previous studies that predominantly emphasize
traditional models, this research employs attention-based deep
learning to capture complex feature interactions, achieving
substantially higher accuracy. The study evaluates nine mod-
els: SVM, Decision Tree, Random Forest, AdaBoost, Gradient
Boosting, XGBoost, KNN, CNN, and Tabular Transformer,
achieving improved accuracy across all models compared to
previous studies, marking a notable advancement in Parkinson’s
disease classification performance. The Transformer’s attention
mechanism captures intricate data patterns, providing clear
advantages over traditional approaches and improving diag-
nostic precision for early-stage Parkinson’s detection. Data
preprocessing included the Synthetic Minority Over-sampling
Technique for class balancing and feature standardization,
with each model, from SVM and Decision Trees to CNN and
XGBoost, optimized through Optuna for optimal performance.
This research offers the medical field a versatile, high-accuracy
framework that aids clinicians in timely and reliable PD
diagnosis, potentially improving patient outcomes and advancing
Parkinson’s diagnostic tools for future clinical use.