EEFSA-SECM: an enhanced ensemble feature selection and stacking ensemble classifier to detect Parkinson’s disease

Overall, this work provides EEFSA-driven stacking as a new and efficient method of feature selection and ensemble learning combination for Parkinson's disease classification. The proposed EEFSA-SECM framework achieves effective classification accuracy, competitive training/testing times, and improved AUC scores on two benchmark datasets, establishing it as an effective and efficient approach for Parkinson's disease diagnosis.