Enhancing prediction accuracy for Parkinson’s disease using advanced machine learning models

Globally, Parkinson’s disease (PD), a neurological disorder, has a negative influence on the health of older adults. PD has no validated means of treatment, and thus, affects both social (i.e., mental) and physical health among individuals who experience PD. Therefore, it is essential to develop new methods for detecting PD to benefit the health of everyone involved. There is an opportunity to improve PD detection and accuracy by leveraging structured data sources and employing machine learning (ML). Ultimately, the goal of this research work is to provide an overall descriptive ML approach to diagnose PD utilizing acoustical data from the UCI Parkinson’s disease dataset and comparing XGBoost, random forest (RF), Support vector machine (SVM), and K-nearest neighbours (KNN), four of the most common algorithms for classifying data, in their ability to classify PD. The research project was conducted in four phases: (1) establishing a baseline model; (2) Stratifying a 10-Fold cross-validation; (3) Balancing the classes with synthetic minority oversampling (SMOTE); and (4) Reducing dimensions with principal component analysis (PCA). XGBoost achieved the highest accuracy (97.22%) among all models for classifying PD across all four phases of the research project, and also the highest Matthews correlation coefficient among all algorithms tested. This proves that XGBoost is better suited to model non-linear relationships. The results of the experiment showed that using SMOTE significantly improved the performance of the ML classification algorithm, especially that of the KNN algorithm. PCA significantly reduced the dataset’s dimensionality without sacrificing discriminative information. The outcomes demonstrated that the ML classification algorithms exhibited apparent performance differences, which diminished after applying SMOTE and PCA. Hence, it can be concluded that all machine learning classification algorithms had statistically similar classifier performance after the use of SMOTE and PCA. The results show that machine learning classification algorithms, particularly XGBoost and KNN, are effective at detecting PD early. The integration of SHAP-based explainability demonstrates that the proposed framework is not only accurate but also interpretable, thereby bridging the gap between predicted and clinically obtained results in PD diagnosis.