Early Diagnosis of Parkinson's Disease Exploting Motor and Non-Motor Symptoms: Results from the PPMI Cohort

Parkinson's is a neurodegenerative disease, with a slow but progressive evolution, which involves some main functions such as the control of movements and balance. Symptoms vary by patient and include motor factors such as tremors and stiffness as well as non-motor symptoms such as cognitive impairment. Its diagnosis is not easy, so it is becoming increasingly necessary to assist doctors in identifying and predicting the disease. Artificial intelligence takes up this challenge and this work proposes a new approach to predict the onset of the disease and monitor patients. The experimentation involved the use of different classification algorithms. The proposed methodology was validated on a large ad hoc data set by compiling data collected by the Parkinson's Progression Markers Initiative (PPMI). Specifically, the study compares the results of the classification taking into consideration only the characteristics belonging to the motor sphere, or those of the non-motor sphere, with the aim of understanding which characteristics are more significant for the identification of the disease. In this regard, a multi-stage feature selection was conducted and SHAP was used to make the model explainable.