Optimizing Predictive Models for Parkinson's Disease Diagnosis

Introduction

Parkinson's disease is a degenerative neurological condition that becomes worse over time and continues to provide difficult problems for researchers in medicine and healthcare providers around. The second most prevalent neurological condition, PD typically affects those over 65. The number of instances of PD is predicted to rise significantly by 2050, raising concerns in wealthy nations due to the high expense of treating the condition (Aich et al., 2019). 90% of PD patients experience speech impairment, a prevalent neurological condition for which there is no treatment (Mathur et al., 2019). This debilitating illness, which affects millions of people globally and negatively impacts their quality of life, is typified by a wide range of movement and other signs and symptoms. While still useful, traditional diagnostic techniques frequently rely on subjective judgments and lack the accuracy required for early identification and customized therapy (Przedborski et al., 2003). Patients with rapid eye movement (REM) sleep behaviour disorder (RBD), who have a high risk of developing PD, are a useful model for researching preclinical PD signs such as mild speech and vocal abnormalities (Rahman et al., 2021). However, there is a ray of optimism that the way we handle Parkinson's disease may change dramatically with the development of Machine Learning (ML). The application of ML, a subfield of artificial intelligence that allows machines to infer conclusions or anticipate from data patterns without explicit programming, offers enormous promise across a wide range of industries, including healthcare. This chapter examines the utilization of ML methods in PD research and provides an overview of its relevance and effects on diagnosis, prognosis, and customized care. ML presents prospective possibilities to improve our comprehension of PD and, consequently, revolutionize its care by utilizing the power of algorithms, data analysis, and enhanced computing capabilities.