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Optimized supervised learning approach to predict Parkinson’s disease with minimal attributes using PPMI Datasets

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Abstract

Researchers can examine numerous ailments and forecast improved treatments using a huge number of medical databases. The Michael Fox PPMI data sets provide a baseline evaluation of the disease using the Unified Parkinson's Disease Rating Scales, the most prevalent sequential scales for identifying Parkinson's conditions. Existing research uses a Gaussian mixture model to predict PD disease using min–max normalization and dimensionality reduction based on principal component analysis. It significantly improved the findings, but it required more data to make predictions. This may be developed by decreasing the numbers of features utilized for prediction utilizing optimization algorithms. As a result, the ant-colony optimizations approach is suggested in this paper to enhance the classifier with few features. The ant colony approach uses this information to select the lowest features to use in training the regression neural network for disease prediction. When compared to various dimensionality reductions methods including Fast-ICA, PCAs, Kernel-PCA, as well as NMF, the findings suggest that the suggested optimization approaches performing-well. The neural regression network also reveals that the suggested strategy outperforms the Gaussian mixture model with the least patient information on the UPDRSs dataset.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The Parkinson's Progression Markers Initiative (PPMI) is a collaborative effort between the public and business sectors. The Michael J. Fox Foundation for Parkinson's Research and its funding partners support PPMI. The PPMI database was utilized to get the information for this article.

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No funding is involved in this work.

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Authors and Affiliations

  1. Department of Information Technology, Kumaraguru College of Technology, Coimbatore, India

    S. Kanagaraj

  2. Department of Computer Science and Engineering, Sri Venkateshwara College of Engineering, Vidya nagar, Bengaluru, 562 157, Karnataka, India

    M. S. Hema

  3. Sri Venkateshwara College of Engineering, Vidya nagar, Bengaluru, 562 157, Karnataka, India

    M. Nageswara Guptha

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  1. S. Kanagaraj
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  2. M. S. Hema
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  3. M. Nageswara Guptha
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All authors are contributed equally to this work.

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Correspondence to S. Kanagaraj.

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Kanagaraj, S., Hema, M.S. & Guptha, M.N. Optimized supervised learning approach to predict Parkinson’s disease with minimal attributes using PPMI Datasets. Multimed Tools Appl 83, 48499–48520 (2024). https://doi.org/10.1007/s11042-023-17582-1

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