ABSTRACT
Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a higher score in males for a certain feature doesn't necessarily mean it's less important for characterizing PD in females. This article proposes an explainable Machine Learning (ML) model to elucidate these underlying factors, emphasizing the importance of features. This insight could be critical for personalized medicine, suggesting the need to tailor data collection and analysis for males and females. The model identifies sex-specific differences in PD, aiding in predicting outcomes as "Healthy" or "Pathological". It adopts a system-level approach, integrating heterogeneous data - clinical, imaging, genetics, and demographics - to study new biomarkers for diagnosis. The explainable ML approach aids non-ML experts in understanding model decisions, fostering trust and facilitating interpretation of complex ML outcomes, thus enhancing usability and translational research. The ML model identifies muscle rigidity, autonomic and cognitive assessments, and family history as key contributors to PD diagnosis, with sex differences noted. The genetic variant SNCA-rs356181 may be more significant in characterizing PD in males. Interaction analysis reveals a greater occurrence of feature interplay among males compared to females. These disparities offer insights into PD pathophysiology and could guide the development of sex-specific diagnostic and therapeutic approaches.
