Vol.32, No.2, 2022, pp.705-722, doi:10.32604/iasc.2022.022037
OPEN ACCESS
ARTICLE
Classification of Parkinson Disease Based on Patient’s Voice Signal Using Machine Learning
  • Imran Ahmed1, Sultan Aljahdali2, Muhammad Shakeel Khan1, Sanaa Kaddoura3,*
1 Riphah International University, Lahore, 54000, Pakistan
2 Department of Computer Science, Taif University, Taif, KSA
3 College of Technological Innovation, Zayed University, Abu Dhabi, UAE
* Corresponding Author: Sanaa Kaddoura. Email:
Received 26 July 2021; Accepted 13 September 2021; Issue published 17 November 2021
Abstract
Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In this work, Parkinson’s disease is classified with the help of human voice signals. Six different machine learning (ML) algorithms are used in the classification: Stochastic Gradient Descent (SGD) Classifier, Extreme Gradient Boosting (XGB) Classifier, Logistic Regression Classifier, Random Forest Classifier, K-Nearest Neighbour (KNN) Classifier, and Decision Tree (DT) Classifier. This research aims to classify Parkinson’s disease using human voice signals and extract essential features to reduce the complexity of the dataset. Then, human voice signals are analyzed to check the voice intensity and spectrum for PD patients. Then, machine learning classifiers are applied to classify the PD patients based on the extracted features. The results show that SGD-Classifier has 91% accuracy, XGB-Classifier has 95% accuracy, Logistic Regression has 91% accuracy, Random Forest shows 97% accuracy, KNN shows 95% accuracy, and Decision Tree has 95% accuracy. Hence, Random Forest has the highest accuracy. The disease can be studied more by looking for more characteristics of PD patients to enhance its proper use in the medical field.
Keywords
SGD-classifier; XGB-classifier; logistic regression; random forest; KNN-classifier; parkinson disease; decision tree classifier
Cite This Article
Ahmed, I., Aljahdali, S., Khan, M. S., Kaddoura, S. (2022). Classification of Parkinson Disease Based on Patient’s Voice Signal Using Machine Learning. Intelligent Automation & Soft Computing, 32(2), 705–722.
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