Optimizing Predictive Models for Parkinson's Disease Diagnosis

Abstract

This chapter explores how using advanced optimization approaches might improve Parkinson's disease (PD) prediction models. The main goal is to improve the predicting abilities of these models to assist in enhancing the accuracy and reliability of PD diagnosis. This chapter explores the adjusting of predictive model elements through the use of optimization approaches, enabling a more efficient study of multi-modal patient data. In order to provide more reliable PD predictions, this optimization-centric strategy aims to improve feature selection, model parameterization, and validation techniques. Additionally, the chapter looks at the broader ramifications of incorporating optimization into predictive modelling for neurodegenerative diseases, offering insight into how it can alter the accuracy of diagnoses and patient treatment strategies.