phat 19(19): e2

Research Article

Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study

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  • @ARTICLE{10.4108/eai.13-7-2018.162806,
        author={Iqra Nissar and Danish Raza Rizvi and Sarfaraz Masood and Aqib Nazir Mir},
        title={Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={5},
        number={19},
        publisher={EAI},
        journal_a={PHAT},
        year={2020},
        month={1},
        keywords={Parkinson’s disease, speech signals, machine learning, classification, feature selection},
        doi={10.4108/eai.13-7-2018.162806}
    }
    
  • Iqra Nissar
    Danish Raza Rizvi
    Sarfaraz Masood
    Aqib Nazir Mir
    Year: 2020
    Voice-Based Detection of Parkinson’s Disease through Ensemble Machine Learning Approach: A Performance Study
    PHAT
    EAI
    DOI: 10.4108/eai.13-7-2018.162806
Iqra Nissar1,*, Danish Raza Rizvi1, Sarfaraz Masood1, Aqib Nazir Mir1
  • 1: Department of Computer Engineering, Jamia Millia Islamia, New Delhi-110025
*Contact email: iqra.nissar13@gmail.com

Abstract

INTRODUCTION: Parkinson's disease (PD) occurs due to the deficiency of dopamine that regulates various activities of the human body. Researchers have identified that voice is an underlying symptom of PD. Recently, Machine learning (ML) has helped in solving problems of computer vision, natural language processing, speech recognition etc.

OBJECTIVES: This paper aims to analyse the effect of feature type selection i.e. MFCC and TQWT on the efficiency of voice based PD detection system along with the use an ensemble learning based classifier for this task.

METHODS: Hence, in this work, various machine learning models, including Logistic Regression, Naive Bayes, KNN, Random Forest, Decision Tree, SVM, MLP, and XGBoost, have been employed and explored for PD detection purpose. The task of Feature selection was also done using minimum-Redundancy and Maximum-Relevance (mRMR) and Recursive Feature Elimination (RFE) techniques.

RESULTS: The results of the XGBoost with mRMR feature selection, outperformed all other models with a high accuracy of 95.39% and precision, recall and F1-score of 0.95 each, when both MFCC and TQWT features were selected.

CONCLUSION: The results obtained strongly support the use of XGBoost model for the voice sample based detection of PD along with mRMR feature selection technique.