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Genetic Associations on Diagnosis of Parkinson's Disease with Non-Genetic Multimodality Tests Based on Feature Ranking

As studies have found evidence of genetic associations to Parkinson's disease (PD), this paper investigates genetic associations on diagnosis of PD using conventional multimodality PD clinical tests instead of expensive genetic sequencing studies. An importance-driven approach is used to rank features from multimodality clinical tests to determine few key features out of the pool of features. Statistical significance tests are then conducted to validate ranked key features for differentiating genetic PD from other studied categories. The ranked key features are input into various machine learning models for the classification of different PD related categories (PD, GENPD, GENUN). The experiment results show that conventional PD clinical tests without expensive genetic sequencing studies can successfully identify genetic associations to PD from other studied categories.