Single and Multi-modal Analysis for Parkinson’s Disease to Detect Its Underlying Factors

Abstract

AbstractParkinson’s disease (PD) is a neurological condition characterized by the disruption of both motor and non-motor functions. Given the absence of a definitive diagnostic method, it is crucial to uncover its root causes. Consequently, individuals displaying symptoms of Parkinson’s disease can promptly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson’s disease and subsequently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medical assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced search techniques to tailor it to our model’s requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on single-treatment approaches and integrating multiple treatments for a comprehensive therapeutic strategy. To analyze these both, we employed five distinct Machine Learning algorithms. Notably, the Support Vector Machine (linear) emerged as the top performer, reaching an accuracy of 100% in both single and multimodality analysis. Furthermore, we employed four tree-based models for feature selection, with the Gradient Boosted Decision Tree excels in identifying the most significant features. Finally, we employed an Artificial Neural Network utilizing these key features, achieving the highest accuracy of 91.41%.