Open Access

ARTICLE

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

E. Bharath^1,*, T. Rajagopalan²

1 Department of Computer Science and Engineering, University College of Engineering, Villupuram, Kakupppam, 605 103, India
2 Department of Science and Humanities, University of College of Engineering, Ariyalur, Kavanur, 621 705, India

* Corresponding Author: E. Bharath. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 1417-1433. https://doi.org/10.32604/iasc.2023.032102

Received 06 May 2022; Accepted 08 June 2022; Issue published 05 January 2023

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.

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Keywords

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