XEMLPD: an explainable ensemble machine learning approach for Parkinson disease diagnosis with optimized features

An explainable ensemble machine learning framework is proposed, XEMLPD, designed to provide both global and local interpretability in PD diagnosis while maintaining high predictive accuracy, and serves as a valuable tool for clinical data scientists in creating more reliable and interpretable CAD systems.