Applicability of Artificial Intelligence in Smart Healthcare Systems for Automatic Detection of Parkinson’s Disease

Harikumar Pallathadka; S.J.R.K. Padminivalli V.; M. Vasavi; P. Nancy; Mohd Naved; Harish Kumar; Samrat Ray

doi:10.24423/cames.2024.557

Harikumar Pallathadka Manipur International University, Ghari, India
S.J.R.K. Padminivalli V. Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, India
M. Vasavi Department of Computer Science and Engineering, R.V.R. & J.C. College of Engineering, Chowdavaram, Andhra Pradesh, India
P. Nancy Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India
Mohd Naved SOIL School of Business Design, Manesar, Haryana, India
Harish Kumar Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia
Samrat Ray Economics, Sunstone Eduversity, Gurgaon, India

DOI: http://dx.doi.org/10.24423/cames.2024.557

Abstract

Parkinson’s disease is associated with memory loss, anxiety, and depression in the brain. Problems such as poor balance and difficulty during walking can be observed in addition to symptoms of impaired posture and rigidity. The field dedicated to making computers capable of learning autonomously, without having to be explicitly programmed, is known as machine learning. An approach to the diagnosis of Parkinson’s disease, which is based on artificial intelligence, is discussed in this article. The input for this system is provided through photographic examples of Parkinson’s disease patient handwriting. Received photos are preprocessed using the relief feature option to begin the process. This is helpful in the process of selecting characteristics for the identification of Parkinson’s disease. After that, the linear discriminant analysis (LDA) algorithm is employed to reduce the dimensions, bringing down the total number of dimensions that are present in the input data. The photos are then classified via radial basis function-support vector machine (SVM-RBF), k-nearest neighbors (KNN), and naive Bayes algorithms, respectively.

Keywords

Parkinson’s disease detection, machine learning, relief algorithm, LDA algorithm, SVM-RBF, accuracy, sensitivity, specificity,

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