Fox- Based Feature Selection Algorithm for Detection of Parkinson's Disease

This research presents a FOX based feature selection method for detecting Parkinson's disease (PD). PD is a progressive neurological ailment that requires early detection for optimal treatment. The proposed method improves diagnostic accuracy by picking a small subset of discriminative features from high-dimensional data, resulting in better model interpretability and computational economy. Extensive research on several PD datasets reveals that the approach outperforms in both feature selection and disease detection accuracy. Comparative analyses using existing machine learning approaches demonstrate the efficacy of the FOX method. Furthermore, the selected features provide important insights into the underlying biomarkers associated with PD pathogenesis. The proposed FOX-based algorithm provides a viable option for early PD detection, with practical applications for improving patient outcomes through timely intervention and individualized treatment regimens.