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• W4313586474 abstract "Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques. Random forest kerb feature selection (RFKFS) selects only 17 features from 754 attributes. The proposed technique uses validation metrics to assess the performance of ML models. The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56% and a precision of 88.02%, a sensitivity of 98.26%, a specificity of 96.06%. The respective validation score has an Non polynomial vector (NPV) of 99.47%, a Geometric Mean (GM) of 97.15%, a Youden’s index (YI) of 94.32%, and a Matthews’s correlation method (MCC) 90.84%. The proposed model is also more robust than other models. It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier." @default.
• W4313586474 created "2023-01-06" @default.
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• W4313586474 date "2023-01-01" @default.
• W4313586474 modified "2023-10-18" @default.
• W4313586474 title "Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection" @default.
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• W4313586474 doi "https://doi.org/10.32604/iasc.2023.032102" @default.
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