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Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms

Author

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  • Md Abu Sayed

    (Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA)

  • Duc Minh Cao

    (Department of Economics, University of Tennessee, Knoxville, TN, USA)

  • MD Tanvir Islam

    (Department of Computer Science, Monroe College, New Rochelle, New York, USA)

  • Maliha Tayaba

    (Department of Computer Science, University of South Dakota, Vermillion, South Dakota, USA)

  • Md Eyasin Ul Islam Pavel

    (Department of Public and Nonprofit Management, University of Texas at Dallas, Dallas, TX, USA)

  • Md Tuhin Mia

    (School of Business, International American University, Angeles, California, USA)

  • Eftekhar Hossain Ayon

    (Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA)

  • Nur Nobe

    (Department of Healthcare Management, Saint Francis College, Brooklyn, New York, USA)

  • Bishnu Padh Ghosh

    (School of Business, International American University, Los Angeles, California, USA)

  • Malay Sarkar

    (Department of Management Science and Quantitative Methods, Gannon University, USA)

Abstract

Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

Suggested Citation

  • Md Abu Sayed & Duc Minh Cao & MD Tanvir Islam & Maliha Tayaba & Md Eyasin Ul Islam Pavel & Md Tuhin Mia & Eftekhar Hossain Ayon & Nur Nobe & Bishnu Padh Ghosh & Malay Sarkar, 2023. "Parkinson's Disease Detection through Vocal Biomarkers and Advanced Machine Learning Algorithms," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 5(4), pages 142-149, October.
    • Handle: RePEc:alk:jcsts0:v:5:y:2023:i:4:id:6337
    DOI: 10.32996/jcsts.2023.5.4.14
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