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Comparative analysis of machine learning techniques for Parkinson’s detection: A review

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Abstract

Parkinson’s disease (PD) causes structural alterations thereby resulting in irreparable motor and non-motor impairments. Machine Learning (ML) has become inevitable for disease detection over the past few years. On the other hand, neuroimaging has become popular for disease diagnosis due to its effective results. Although recently this amalgamation has been widely used by researchers for Parkinson’s detection, they are yet disseminated widely across the web. Hence, this study has been conducted to provide an overview of the recent findings in Parkinson’s diagnosis using neuroimaging and ML techniques. A systematic literature review has been conducted as per the PRISMA guidelines. The latest studies involving popular neuroimaging modalities namely: Positron Emission Tomography (PET), Single-Photon Emission Computerized Tomography (SPECT), Magnetic Resonance Imaging (MRI) and its sequences have been included in this review. Relevant information regarding the ML approaches, dataset and their findings have also been described in this work. Moreover, the limitations of existing work, recommendations and future perspectives have also been presented. This study concludes that ML-based techniques may have a high potential for an early, effective and informed PD diagnosis if utilized diligently. Hence, it paves the way for neuroimaging along with ML for an effective clinical diagnosis of PD.

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  1. J. C. Bose University of Science and Technology, YMCA, Faridabad, Haryana, 121002, India

    Ketna Khanna & Sapna Gambhir

  2. Ministry of Education, Delhi, 110001, India

    Mohit Gambhir

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Khanna, K., Gambhir, S. & Gambhir, M. Comparative analysis of machine learning techniques for Parkinson’s detection: A review. Multimed Tools Appl 82, 45205–45231 (2023). https://doi.org/10.1007/s11042-023-15414-w

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