  Parkinson's disease (PD) is a debilitating neurological disorder that
necessitates precise and early diagnosis for effective patient care. This study
aims to develop a diagnostic model capable of achieving both high accuracy and
minimizing false negatives, a critical factor in clinical practice. Given the
limited training data, a feature selection strategy utilizing ANOVA is employed
to identify the most informative features. Subsequently, various machine
learning methods, including Echo State Networks (ESN), Random Forest, k-nearest
Neighbors, Support Vector Classifier, Extreme Gradient Boosting, and Decision
Tree, are employed and thoroughly evaluated. The statistical analyses of the
results highlight ESN's exceptional performance, showcasing not only superior
accuracy but also the lowest false negative rate among all methods.
Consistently, statistical data indicates that the ESN method consistently
maintains a false negative rate of less than 8% in 83% of cases. ESN's capacity
to strike a delicate balance between diagnostic precision and minimizing
misclassifications positions it as an exemplary choice for PD diagnosis,
especially in scenarios characterized by limited data. This research marks a
significant step towards more efficient and reliable PD diagnosis, with
potential implications for enhanced patient outcomes and healthcare dynamics.
