A Comparison of Machine Learning-Based Classification and Recognition Methods for Parkinson's Prediction

Abstract

Parkinson's disease (PD) is a neurodegenerative disease that is often associated with abnormal voice function. Early recognition of speech features is crucial for the diagnosis and intervention of PD, and the development of efficient and reliable early screening tools has become a research hotspot in clinical medicine. Current research mostly focuses on single model optimization and lacks a multidimensional assessment framework. This study compares the classification performance of three machine learning models, Logistic Regression, XGBoost and Support Vector Machine (SVM), in PD prediction based on the publicly available Parkinson's speech feature dataset from UCI. Key features are extracted through data feature engineering, oversampling is used to address data imbalance, and metrics such as confusion matrices are used for multidimensional assessment. The results of the study showed that logistic regression had the best overall performance on the validation set, with an accuracy of 81.47% and a recall rate of 95% for healthy people, but its patient recall rate (71%) still needs to be optimized. XGBoost does not adequately deal with class imbalance, under-recognizes minority classes, and SVM performs poorly with the current data distribution. This study provides a theoretical basis for model selection for disease diagnosis and lays down a methodology for intelligent processing of multimodal medical data.