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Performance evaluation of Dictionary Learning and ICA on Parkinson’s patients classification using Machine Learning

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Abstract

Currently, extensive research is being conducted in the application of Machine Learning (ML) algorithms in the medical domain and one such area is classifying Parkinson’s Patients. Feature extraction methods play a critical part in the use of ML techniques in providing better accuracies. The earlier studies have used various feature extraction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), Region of Interest (ROIs), etc. This paper deals with the use of two feature extraction techniques - Canonical Independent Component Analysis (CanICA) and Dictionary Learning (DL) for the Functional Magnetic Resonance Imaging (fMRI) modality. Region of Interest (ROI) extraction of connected components and dimensionality reduction algorithms further refine the features. The features extracted are then applied to the Machine Learning models for the classification of individuals suffering from Parkinson’s disease. The methodology adopted in the research provided accuracy of 87.5% and 86.6% using the CanICA and DL techniques respectively. The accuracies obtained are found to be better than the other research conducted using ML algorithms for the MRI data.

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Data availability

The data is freely available online and the link to the data is provided in the Acknowledgement.

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Acknowledgements

The database that is used in the conduction of the described research is obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data). PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners.

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Authors and Affiliations

  1. The Northcap University, Gurgaon, Haryana, India

    Saloni Bhatia Dutta

  2. CSE Department, The Northcap University, Gurgaon, Haryana, India

    Rekha Vig

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Correspondence to Saloni Bhatia Dutta.

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Dutta, S.B., Vig, R. Performance evaluation of Dictionary Learning and ICA on Parkinson’s patients classification using Machine Learning. Multimed Tools Appl 83, 24467–24483 (2024). https://doi.org/10.1007/s11042-023-16485-5

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  • DOI: https://doi.org/10.1007/s11042-023-16485-5

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