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Speech-Based Parkinson’s Disease Prediction Using XGBoost-Based Features Selection and the Stacked Ensemble of Classifiers
https://orcid.org/http://orcid.org/0000-0002-0654-782X
Parkinson’s disease (PD) is a neuron-related disorder due to the decrease in dopaminergic neurons present in the midbrain. For the last few decades, speech is an emerging interest in the analysis and detection of PD. In this study, a predictive machine learning framework based on extreme gradient boosting (XGBoost) features selection and a stacked ensemble approach is presented to investigate the voice tremor of people suffering from PD. The proposed framework consists of two stages: In the first stage the optimized features are obtained using XGBoost features selection, and in the second stage a PD detection system is developed using stacked ensemble classifiers. Leave one subject out (LOSO) cross-validation shows that the proposed framework gives average accuracy of up to 95.07% compared to results obtained with individual classifiers. Additionally, it was also concluded that reduced features had given the highest classification accuracy compared to the raw features set which saves training time and enhances the prediction accuracy.
Speech-Based Parkinson’s Disease Prediction Using XGBoost-Based Features Selection and the Stacked Ensemble of Classifiers
https://orcid.org/http://orcid.org/0000-0002-0654-782X
Parkinson’s disease (PD) is a neuron-related disorder due to the decrease in dopaminergic neurons present in the midbrain. For the last few decades, speech is an emerging interest in the analysis and detection of PD. In this study, a predictive machine learning framework based on extreme gradient boosting (XGBoost) features selection and a stacked ensemble approach is presented to investigate the voice tremor of people suffering from PD. The proposed framework consists of two stages: In the first stage the optimized features are obtained using XGBoost features selection, and in the second stage a PD detection system is developed using stacked ensemble classifiers. Leave one subject out (LOSO) cross-validation shows that the proposed framework gives average accuracy of up to 95.07% compared to results obtained with individual classifiers. Additionally, it was also concluded that reduced features had given the highest classification accuracy compared to the raw features set which saves training time and enhances the prediction accuracy.
Speech-Based Parkinson’s Disease Prediction Using XGBoost-Based Features Selection and the Stacked Ensemble of Classifiers
https://orcid.org/http://orcid.org/0000-0002-0654-782X
Parkinson’s disease (PD) is a neuron-related disorder due to the decrease in dopaminergic neurons present in the midbrain. For the last few decades, speech is an emerging interest in the analysis and detection of PD. In this study, a predictive machine learning framework based on extreme gradient boosting (XGBoost) features selection and a stacked ensemble approach is presented to investigate the voice tremor of people suffering from PD. The proposed framework consists of two stages: In the first stage the optimized features are obtained using XGBoost features selection, and in the second stage a PD detection system is developed using stacked ensemble classifiers. Leave one subject out (LOSO) cross-validation shows that the proposed framework gives average accuracy of up to 95.07% compared to results obtained with individual classifiers. Additionally, it was also concluded that reduced features had given the highest classification accuracy compared to the raw features set which saves training time and enhances the prediction accuracy.
Speech-Based Parkinson’s Disease Prediction Using XGBoost-Based Features Selection and the Stacked Ensemble of Classifiers
J. Inst. Eng. India Ser. B
Karan, Biswajit (author)
Journal of The Institution of Engineers (India): Series B ; 104 ; 475-483
2023-04-01
9 pages
Article (Journal)
Electronic Resource
English
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