Intelligent Models for Early Parkinson’s Disease Prediction: A Systematic Review

Abstract

Parkinson’s disease (PD) is a neurodegenerative brain condition that significantly impairs behavior, movement and speech. The Hoehn and Tahr staging scale is used to assess the extent of the condition. These evaluations can be costly, unpredictable, and time-consuming for individuals. Hence, the need to explore innovative approaches for diagnosis to enhance clinical results is highlighted by the lack of a definitive therapy. By using massive databases of organized data to improve diagnosis accuracy, artificial intelligence (AI) offers an opportunity to completely reinvent PD identification. Therefore, the purpose of this paper is to perform a systematic review of the literature with an emphasis on the significant advancements being made in this domain of research. The accessible existing research works published from 2022 to 2026 are gathered and examined in order to accomplish the analysis’s goal. Additionally, this review offers a thorough analysis of current research with an emphasis on various PD markers, adapted strategies and outcome measures. The considered articles are compared on the basis of their objective, databases, data type, utilized AI approaches and result. The results show an extensive variety of research conducted globally and a substantial advancement of PD detection employing AI techniques in recent years. Moreover, this research reveals a great deal of opportunity in employing fresh indicators and AI techniques in healthcare decisions, which could result in a more methodical and accurate identification of PD.