Prediction of Parkinson's Disorder: A Machine Learning Approach

Patnaik, Debasis; Henriques, Mavis; Laurel, Ashin

doi:10.1109/irtm54583.2022.9791490

Cited by 1 publication

(1 citation statement)

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“…The PSD symptoms are similar to other disorders, such as corticobasal syndrome and essential tremor. Thus, it becomes challenging to identify the PSD patients from other disorders and HC [ 2 ]. The diagnosis of a disease is usually evaluated using clinical and laboratory tests, so there is a chance of misdiagnosis.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Parkinson’s disease (PSD) is a neurological disorder of the brain where nigrostriatal integrity functions lead to motor and non-motor-based symptoms. Doctors can assess the patient based on the patient’s history and symptoms; however, the symptoms are similar in various neurodegenerative diseases, such as progressive supranuclear palsy (PSP), multiple system atrophy—parkinsonian type (MSA), essential tremor, and Parkinson’s tremor. Thus, sometimes it is difficult to identify a patient’s disease based on his or her symptoms. To address the issue, we have used neuroimaging biomarkers to analyze dopamine deficiency in the brains of subjects. We generated the different patterns of dopamine levels inside the brain, which identified the severity of the disease and helped us to measure the disease progression of the patients. For the classification of the subjects, we used machine learning (ML) algorithms for a multivariate classification of the subjects using neuroimaging biomarkers data. In this paper, we propose a stacked machine learning (ML)-based classification model to identify the HC and PSD subjects. In this stacked model, meta learners can learn and combine the predictions from various ML algorithms, such as K-nearest neighbor (KNN), random forest algorithm (RFA), and Gaussian naive Bayes (GANB) to achieve a high performance model. The proposed model showed 92.5% accuracy, outperforming traditional schemes.

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Section: Introductionmentioning

confidence: 99%