The Utilization of An Artificial Intelligence and Machine Learning Model to Aid in Accelerating Parkinson’s Disease Diagnosis

Parkinson’s Disease (PD) is a progressive neurodegenerative disease that has no known cure. PD’s treatment largely relies on early and accurate diagnosis, which is currently based on clinical evaluations, neurological exams, patient histories and the observation of motor symptoms, such as bradykinesia, tremors, and rigidity. However, due to the overlap of these symptoms with other neurological conditions and the lack of conclusive diagnostic tests, PD diagnosis is often prolonged (up to years) and may be inaccurate. This research explores the use of Random Forest, a machine learning (ML) algorithm, to enhance the speed and accuracy of PD diagnosis. Our model has been trained on a medical dataset consisting of patient demographics and biomarker data, as they are considered some of the earliest indicators of the disease. The Random Forest model allows for powerful pattern recognition by integrating multiple decision trees, offering reliable diagnostic support for current clinical practices. Our findings demonstrate that ML-driven approaches have the potential to accelerate PD detection while preserving a high degree of accuracy, offering promising supplementary tools to traditional diagnostic methods.