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A deep learning approach for classification and diagnosis of Parkinson’s disease

  • Data analytics and machine learning
  • Published:
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Abstract

Deep learning grabs a center attraction in industries, deep learning techniques are having great potential and recently these potentials are applied to healthcare problems, including computer-aided detection/diagnosis, disease prediction. Deep learning techniques are playing an important role in the classification and prediction of the diseases. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating the reports. In this paper, we have proposed a Deep Neural Network-based classification model for the classification of Parkinson’s disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson’s disease patients with excellent accuracy of 94.87%. We have also compared the results with other existing approaches like linear discriminant analysis, support vector machine, K-nearest neighbor, decision tree, classification and regression trees, random forest, linear regression, logistic regression, multi-layer perceptron, and Naive Bayes.

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Funding

This research is funded by UGC under the scheme RGNF-SC funded by the Ministry of social justice empowerment and ministry of tribal affairs of India, New Delhi, India.

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Authors and Affiliations

  1. Central University of Rajasthan, Ajmer, Rajasthan, India

    Monika Jyotiyana & Nishtha Kesswani

  2. Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Munish Kumar

Authors
  1. Monika Jyotiyana
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  2. Nishtha Kesswani
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  3. Munish Kumar
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Contributions

MJ: Conceptualization, formal analysis, data curation, writing—original draft. NK: project administration, formal analysis, resources, writing—original draft, supervision, writing—review and editing. MK: review and editing, Supervision, project administration.

Corresponding author

Correspondence to Munish Kumar.

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No human and animal participants were used.

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Appendix A: Proposed algorithm

Appendix A: Proposed algorithm

In this sub-section, we discuss the proposed Algorithm that classifies the subject with Status 1 indicating that the subject is suffering from Parkinson’s and Status 0 indicating that the subject is Healthy (Table

Table 7 Notations used in algorithm 1
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7).

figure a

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Jyotiyana, M., Kesswani, N. & Kumar, M. A deep learning approach for classification and diagnosis of Parkinson’s disease. Soft Comput 26, 9155–9165 (2022). https://doi.org/10.1007/s00500-022-07275-6

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