Frontiers in neurology · 2026
EEFSA-SECM: an enhanced ensemble feature selection and stacking ensemble classifier to detect Parkinson's disease.
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Rajput Vridhi, Maheswari N
Аннотация
INTRODUCTION: Parkinson's disease (PD) is a progressive neurological disorder whose early symptoms often remain undetected, making timely diagnosis challenging. Machine learning offers strong algorithms to detect subtle speech-based biomarkers that are impossible to detect by standard methods.
METHODS: In this article, we proposed an Enhanced Ensemble Feature Selection Algorithm (EEFSA) which combines filter, wrapper, and embedded approaches to extract the best informative features, eliminate redundancy, and improve classification performance. The proposed work has tested on two benchmark audio based datasets, such as Dataset-1 (46 features, 80 samples), where EEFSA reduced the features to 20 features, and Dataset-2 (754 features, 252 samples), where EEFSA reduced the features to 40 features. Nine machine learning classifiers were tried out and the best of them were combined into a stacking ensemble with logistic regression as the meta-classifier.
RESULTS: Experiments show that EEFSA-driven dimensionality reduction not only enhanced accuracy of classification but also reduced training time considerably and minimized over fitting. The Stacking Ensemble Classifier Model (SECM) deployed on the basis of the proposed method achieved accuracy of 86.67% and 89.95% on Dataset-1 and Dataset-2, respectively, and outperformed individual classifiers in all experiments.
CONCLUSION: 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.
METHODS: In this article, we proposed an Enhanced Ensemble Feature Selection Algorithm (EEFSA) which combines filter, wrapper, and embedded approaches to extract the best informative features, eliminate redundancy, and improve classification performance. The proposed work has tested on two benchmark audio based datasets, such as Dataset-1 (46 features, 80 samples), where EEFSA reduced the features to 20 features, and Dataset-2 (754 features, 252 samples), where EEFSA reduced the features to 40 features. Nine machine learning classifiers were tried out and the best of them were combined into a stacking ensemble with logistic regression as the meta-classifier.
RESULTS: Experiments show that EEFSA-driven dimensionality reduction not only enhanced accuracy of classification but also reduced training time considerably and minimized over fitting. The Stacking Ensemble Classifier Model (SECM) deployed on the basis of the proposed method achieved accuracy of 86.67% and 89.95% on Dataset-1 and Dataset-2, respectively, and outperformed individual classifiers in all experiments.
CONCLUSION: 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.