Machine learning-based approach to improving Parkinson’s disease diagnosis through voice signal analysis

Abstract

Parkinson’s disease (PD) is a chronic neurodegenerative disorder that influences motor and non-motor function and imposes huge health-related costs on society. Early and precise diagnosis still presents a big challenge because clinical examinations are subjective, and diagnostic methods based on imaging are expensive and invasive. As the vocal deficiencies are usually present in early PD, voice is considered to be a potential non-invasive and widely available neurobiological marker for early diagnosis. This thesis presents an end-to-end framework for PD detection via ML and XAI. Voice datasets were preprocessed for normalization, class balancing and dimensionality reduction in order to improve data quality. Acoustic Feature Selection We used a range of feature selection methods, including statistical significance testing, SelectKBest ranking, and principal component analysis to extract discriminative sub-sets of acoustic features. Various machine learning models were trained and tested, the ensemble methods performed well. The results indicate that the oversight model based on the selection of only 100 picked features with XGBoost approach provides high accuracy, precision and recall as well as F1-scores greater than 0.85 on the test set. SHAP and Morris Sensitivity Analysis were used to increase the interpretablity, revealing important indicators of PD-related vocal changes including jitter, shimmer and MFCC features. These observations were consistent with clinical data, which linked computational modeling to biological knowledge. The results indicate that voice coupled with interpretable ML techniques can be used as a robust digital biomarker for PD. This work will help build scalable, accessible and explainable tools for early diagnosis and remote healthcare.