Predicting Parkinson’s Disease Using Multimodal Data and Machine Learning Techniques

Parkinson’s Disease (PD) is a degenerative neurological disease that has significant worldwide implications, impacting a considerable number of individuals across the globe. Timely identification of disease plays a critical role in easy and prior intrusion and improving patient health. The paper deliberates a comprehensive study that leverages Machine Learning (ML) algorithms to generate accurate models for detecting PD. Various ML techniques like decision trees, SVM, random forests etc., are employed in the analysis. The study utilizes a diverse dataset comprising clinical and demographic features of patients, including motor symptoms, medical history, and genetic information. The results display the efficacy of ML algorithms in discrimination between PD and healthy individuals. The models exhibit high accuracy, sensitivity, and specificity in identifying the disease. Furthermore, the predictive models developed in the study showcase promising performance in estimating disease progression and severity. This research’s discoveries add to the expanding knowledge base regarding the utilization of ML in PD’s diagnosis and prognosis.