An Optimized and Robust Machine Learning Framework for Early Parkinson's Disease Prediction Using Speech Signals

With the rapid advancement of technologies in the present era, predicting Parkinson's disease (PD) early using non-invasive and low-cost methods, such as speech analysis with machine learning (ML) tools, remains a challenging task and lacks sufficient confidence for healthcare providers to use in daily practice. Therefore, this study presents an optimized early PD prediction tool and investigates its stability and robustness using a rigorous evaluation mechanism. For early PD prediction using speech signal data, the eXtreme Gradient Boosting (XGB) model is optimized using the Tree-structured Parzen Estimator (TPE) method and the Synthetic Minority Oversampling Technique (SMOTE) for solving the imbalanced dataset problem. Its performance was rigorously evaluated using an optimized strategy to ensure reliability and to earn the trust of clinicians for real-world operational use. To validate the model's trustworthiness and prediction capability, it was evaluated through ۱۰ different runs of Stratified ۱۰-Fold Cross Validation (SCV). The average measures of accuracy as ۹۶.۷۶%, precision as ۹۷.۷۰%, f۱-score as ۹۶.۷۰%, recall as ۹۵.۹۱% and ROC-AUC ۹۸.۷۲% show great progress and performance in comparison with similar works. The model performance and stability were evaluated in many different situations and showed that the proposed model is stable and strong enough, and could be used as a practical tool in daily medical care. This tool brings the opportunity to be used easily as a decision support system through a website and detect PD early using patient voice signal with low cost in a non-invasive way that could be used remotely and easily. With the rapid advancement of technologies in the present era, predicting Parkinson's disease (PD) early using non-invasive and low-cost methods, such as speech analysis with machine learning (ML) tools, remains a challenging task and lacks sufficient confidence for healthcare providers to use in daily practice. Therefore, this study presents an optimized early PD prediction tool and investigates its stability and robustness using a rigorous evaluation mechanism. For early PD prediction using speech signal data, the eXtreme Gradient Boosting (XGB) model is optimized using the Tree-structured Parzen Estimator (TPE) method and the Synthetic Minority Oversampling Technique (SMOTE) for solving the imbalanced dataset problem. Its performance was rigorously evaluated using an optimized strategy to ensure reliability and to earn the trust of clinicians for real-world operational use. To validate the model's trustworthiness and prediction capability, it was evaluated through ۱۰ different runs of Stratified ۱۰-Fold Cross Validation (SCV). The average measures of accuracy as ۹۶.۷۶%, precision as ۹۷.۷۰%, f۱-score as ۹۶.۷۰%, recall as ۹۵.۹۱% and ROC-AUC ۹۸.۷۲% show great progress and performance in comparison with similar works. The model performance and stability were evaluated in many different situations and showed that the proposed model is stable and strong enough, and could be used as a practical tool in daily medical care. This tool brings the opportunity to be used easily as a decision support system through a website and detect PD early using patient voice signal with low cost in a non-invasive way that could be used remotely and easily.

کلیدواژه ها:

XGBoost ، Tree-structured Parzen Estimator ، Data Augmentation ، SMOTE ، Decision Support System