Selection of Gait Parameters for Differential Diagnostics of Patients With De Novo Parkinson's Disease

Abstract

Early detection of a disease empowers medical professionals to initiate interventions at a stage when treatment can be most effective, enhancing the well-being of patients. Unfortunately, Parkinson's disease (PD) remains notorious for its difficult detection in preliminary stages, resulting in delayed treatment and poor patient outcomes. Gait analysis along with machine learning plays a critical role in the early and accurate diagnosis of PD, revolutionizing how we detect and manage this disorder. Machine learning algorithms, when fed with vast amounts of gait data, can effectively learn to detect patterns indicative of PD with accuracy. These algorithms can analyze subtle gait features that clinicians may find difficult to recognize, resulting in more trustworthy and objective judgements. Therefore, in this chapter, the authors delve into the critical significance of machine learning in early identification of de novo Parkinson's disease by utilizing gait analysis as well as parameter selection for smooth algorithm performance.