Exploring a Novel Machine Learning Approach for Evaluating Parkinson's Disease, Duration, and Vitamin D Level

Abstract

Parkinson's disease is an increasingly prevalent, degenerative neurological condition predominantly afflicting individuals aged 50 and older. As global life expectancy continues to rise, the imperative for a deeper comprehension of factors influencing the course and intensity of PD becomes more pronounced. This investigation delves into these facets, scrutinizing various parameters including patient medical history, dietary practices, and vitamin D levels. A dataset comprising 50 PD patients and 50 healthy controls, sourced from Dhaka Medical Institute, serves as the foundation for this study. Machine learning techniques, notably the Modified Random Forest Classifier (MRFC), are harnessed to prognosticate both PD severity and duration. Strikingly, the MRFC-based prediction model for PD severity attains an impressive accuracy of 97.14%, while the predictive model for PD duration demonstrates an accuracy of 95.16%. Noteworthy is the observation that vitamin D levels are notably higher in the healthy cohort compared to PD-afflicted individuals, exerting a substantial positive influence on both the severity and duration predictions, surpassing the influence of other measured parameters. This inquiry underscores the practicality of machine learning in forecasting PD progression and duration and underscores the pivotal role of vitamin D levels as a predictive factor. These discoveries provide invaluable insights into advancing our comprehension and management of PD in an aging population.