A Comprehensive Framework for Parkinson's Disease Detection Using XG Boost Classifier and Data Driven Machine Learning Methods

Parkinsons disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor manifestations that can make early and objective detection difficult. Conventional diagnosis primarily depends on clinical examination and longitudinal assessment, creating opportunities for data-driven machine learning (ML) approaches to provide quantitative decision support. This review examines the development of ML-based PD detection with particular emphasis on extreme gradient boosting (XGBoost), feature-engineered clinical models, speech and handwriting analysis, gait and wearable sensing, neuroimaging, and multimodal learning. Existing studies demonstrate that XGBoost is particularly suitable for heterogeneous tabular biomedical data because it can model nonlinear feature interactions, accommodate mixed feature distributions, provide feature importance estimates, and operate effectively with relatively structured datasets. However, reported performance is strongly influenced by dataset size, feature selection, class balancing, validation design, and data leakage. Recent systematic reviews further indicate inconsistent use of independent test sets, hyperparameter optimization, external validation, and direct model comparison. The review therefore synthesizes the current evidence into a data-driven PD detection framework integrating multimodal acquisition, preprocessing, feature selection, XGBoost-based classification, explainable artificial intelligence, uncertainty estimation, and external validation. Finally, the paper identifies research gaps involving dataset heterogeneity, clinical generalization, fairness, reproducibility, multimodal fusion, and real-world deployment, and proposes a roadmap toward clinically interpretable and robust PD decision-support systems.