Abstract
Parkinson's disease (PD) is a complex neurodegenerative disease. Accurate diagnosis of this disease in the early stages is crucial for its initial treatment. This paper aims to present a comparative study on the methods developed by machine learning techniques in PD diagnosis. We rely on clustering and prediction learning approaches to perform the comparative study. Specifically, we use different clustering techniques for PD data clustering and support vector regression ensembles to predict Motor-UPDRS and Total-UPDRS. The results are then compared with the other prediction learning approaches, multiple linear regression, neurofuzzy, and support vector regression techniques. The comparative study is performed on a real-world PD dataset. The prediction results of data analysis on a PD real-world dataset revealed that expectation-maximization with the aid of SVR ensembles can provide better prediction accuracy in relation to decision trees, deep belief network, neurofuzzy, and support vector regression combined with other clustering techniques in the prediction of Motor-UPDRS and Total-UPDRS.
Highlights
Parkinson’s disease (PD) is the second most common and complex neurodegenerative disorder worldwide [1,2,3,4]
Note that the success and effectiveness of ensemble learning approaches are heavily dependent on the diversity of the individual predictors that construct the ensemble. e total error can be reduced by combining the output of different prediction models through an algebraic expression, as the various errors of the prediction models are averaged out
SVR performs the prediction of a new sample by training the data with target values. is is done by finding Φ(x) function to map data to a flat space. e SVR can effectively solve complex prediction problems through linear and nonlinear regression. e kernel functions are used to transform the data into a high-dimensional feature space
Summary
Parkinson’s disease (PD) is the second most common and complex neurodegenerative disorder worldwide [1,2,3,4]. UPDRS includes 4 sections, in which UPDRS I, UPDRS II, UPDRS III, and UPDRS IV are used to evaluate psychiatric symptoms in PD, activities of daily living, reliable motor symptoms measured in PD recognized by physical exam, and complications of treatment [10]. In many studies, this scale is considered
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