CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES
Oumaima Majdoubi
oumaima_majdoubi@um5.ac.maMohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
https://orcid.org/0009-0000-2968-7975
Achraf Benba
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
http://orcid.org/0000-0001-7939-0790
Ahmed Hammouch
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
http://orcid.org/0009-0005-8691-6662
Abstract
This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications.
Keywords:
voice analysis, Parkinson’s disease, MFCC, PCA, naive Bayes kernel, machine learningReferences
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Authors
Oumaima Majdoubioumaima_majdoubi@um5.ac.ma
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
https://orcid.org/0009-0000-2968-7975
Authors
Achraf BenbaMohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
http://orcid.org/0000-0001-7939-0790
Authors
Ahmed HammouchMohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
http://orcid.org/0009-0005-8691-6662
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