PARKINSON'S PROGNOSTICATION: MACHINE LEARNING PREDICTIVE MODELING FOR EARLY DIAGNOSIS

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, making its early diagnosis challenging yet critical for effective treatment and management.  The application of machine learning-based predictive modeling for Parkinson's disease diagnosis has significant implications for healthcare and clinical practice. Accurate and early diagnosis of PD enables timely intervention and treatment planning, facilitating better patient outcomes and quality of life. Additionally, machine learning models can assist healthcare professionals in screening individuals at risk for PD, potentially leading to earlier detection and intervention. Moreover, these models can support research efforts aimed at understanding the underlying mechanisms of PD progression and identifying novel biomarkers for disease diagnosis and monitoring. Traditional methods for PD diagnosis often rely on clinical assessment tools and subjective evaluations, which may lack sensitivity and specificity, particularly in the early stages of the disease. These methods typically involve manual scoring of motor symptoms and may overlook subtle changes in speech patterns and vocal characteristics associated with PD. Moreover, clinical assessments may be time-consuming and require specialized expertise, limiting their scalability and accessibility in primary care settings. Additionally, traditional diagnostic approaches may fail to utilize the wealth of information available in voice recordings, such as detailed acoustic features and nonlinear dynamics, leading to suboptimal diagnostic accuracy. The proposed system aims to overcome the limitations of traditional diagnostic methods by leveraging machine learning techniques for PD diagnosis using voice data. This work employs supervised learning algorithms to train predictive models on the dataset of voice features. By extracting informative features and learning complex patterns from the data, the proposed models can effectively distinguish between individuals with PD and healthy conditions.