Multinomial logistic regression algorithm for the classification of patients with parkinsonisms.

ABSTRACT:

Background

Accurate differential diagnosis of neurodegenerative parkinsonisms is challenging due to overlapping early symptoms and high rates of misdiagnosis. To improve the diagnostic accuracy, we developed an integrated classification algorithm using multinomial logistic regression and Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) applied to ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images. In this novel classification approach, SSM/PCA is applied to FDG-PET brain images of patients with various parkinsonisms, which are compared against the constructed undetermined images. This process involves spatial normalization of the images and dimensionality reduction via PCA. The resulting principal components are then used in a multin

SUBMITTER: Stokelj E 

PROVIDER: S-EPMC11911283 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature