Stacked Model-Based Classification of Parkinson’s Disease Patients Using Imaging Biomarker Data

. 2022 Jul 29;12(8):579. doi: 10.3390/bios12080579

Algorithm 2 Execution process of the proposed model

Input: Imaging biomarkers dataset is collected from PPMI

Output: Classification of PSD and HC subject

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procedurePre-Processing data(:)
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X $\overset{}{\leftarrow}$ Input data
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X = MIN-MAX Normalization (X)
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X = FA (X)
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end procedure
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procedureModel Training phase(:)
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GANB $\overset{}{\leftarrow}$ GANB (X)
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RFA $\overset{}{\leftarrow}$ RFA algorithm (X)
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KNN $\overset{}{\leftarrow}$ K Neighbors algorithm (X)
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Classifier algorithm $\overset{}{\leftarrow}$ [GANB, RFA, KNN]
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Meta Learning classifier algorithm $\overset{}{\leftarrow}$ Logistic regression algorithm ()
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Stacked $\overset{}{\leftarrow}$ Stacking classifiers(Classifier, Meta-Learning classifier)
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end procedure
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procedureTesting and evaluation Phase(:)
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for <i in models> do
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Model. fit (X)
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Predicted Output $\overset{}{\leftarrow}$ Model.predict()
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Accuracy $\overset{}{\leftarrow}$ Model.evaluate()
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end for
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end procedure