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Speech features-based Parkinson’s disease classification using combined SMOTE-ENN and binary machine learning

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Abstract

Purpose

Parkinson’s disease (PD) is one of the most prevalent neurodegenerative diseases in the global context. The presently available detecting process of PD is costly and labour-intensive. Along with a movement disorder, PD also affects speech differently. By causing variation in pitch, monotonicity, slurring of words, or slow speed of talking. This study uses different machine learning binary classification algorithms for the detection and classification of PD.

Methods

The publicly available Parkinson’s disease speech features dataset is imbalanced, with only 192 healthy instances compared to 564 PD instances. Synthetic Minority Oversampling Technique – Edited Nearest Neighbours (SMOTE-ENN) algorithms rectify the class imbalance by oversampling and under sampling. Thus, it results in a balanced dataset free of noisy samples. Machine learning binary classifiers, Random Forest, K-Nearest Neighbours, Support Vector Machine, Extreme Gradient Boosting, Decision Tree, and Logistic Regression are investigated.

Results

The classification algorithms have been analysed and compared based on several standard evaluation metrics. The classification model, resampling using SMOTE-ENN technique, and dimensionality reduction using principal component analysis (PCA) have been performed on the dataset to enhance the performance and prevent overfitting. The combination of SMOTE-ENN and Support Vector Machine (SVM) yields a good accuracy of 96.5%.

Conclusion

Speech features are a predictive and non-intrusive method, thus making the diagnostic process painless and straightforward. The reported results are promising to aid the diagnosis of PD so that treatment can be administered as early as possible. Thus, the primary findings are beneficial to detect PD at an early stage with optimal accuracy.

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Availability of data and materials

The data analyzed in this study are available here. https://tinyurl.com/n24jt4vz.

Abbreviations

ANN:

Artificial neural networks

AUC:

Area under the curve

EC:

Electrocardiogram

ENN:

Edited nearest neighbours

FN:

False negative

FP:

False positive

FPR:

False positive rate

KNN:

K-Nearest neighbours

MFCC:

Mel frequency cepstral coefficient

ML:

Machine learning

PCA:

Principal component analysis

PD:

Parkinson’s disease

RBF:

Radial basis function

ROC:

Receiver operating characteristics

RF :

Random Forest

SMOTE:

Synthetic minority oversampling technique

SVM:

Support vector machine

TN:

True negative

TP:

True positive

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Acknowledgements

The authors are grateful to the SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India.

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No funding was received.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Samiappan Dhanalakshmi & Sudeshna Das

  2. Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    Ramalingam Senthil

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  1. Samiappan Dhanalakshmi
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Contributions

Conceptualization, Methodology, Formal analysis, Writing-Original draft: Samiappan Dhanalakshmi and Sudeshna Das; Investigation, Validation, Visualization, Reviewing and Editing: Samiappan Dhanalakshmi, Sudeshna Das and Ramalingam Senthil. All authors were involved in the preparation of subsequent drafts and made substantial contributions. In addition, all authors approved the final version.

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Correspondence to Ramalingam Senthil.

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Dhanalakshmi, S., Das, S. & Senthil, R. Speech features-based Parkinson’s disease classification using combined SMOTE-ENN and binary machine learning. Health Technol. 14, 393–406 (2024). https://doi.org/10.1007/s12553-023-00810-x

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