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Prediction of Parkinson’s Disease Using Machine Learning Methods - PMC

pmc.ncbi.nlm.nih.gov · 8,598 words · saved by 1 readers

Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Primary site navigation Logged in as: Correspondence: biostat-ll@sxmu.edu.cn (L.L.); y.wen@auckland.ac.nz (Y.W.) Received 2023 Oct 9;

Abstract The detection of Parkinson’s disease (PD) in its early stages is of great importance for its treatment and management, but consensus is lacking on what information is necessary and what models should be used to best predict PD risk. In our study, we first grouped PD-associated factors based on their cost and accessibility, and then gradually incorporated them into risk predictions, which were built using eight commonly used machine learning models to allow for comprehensive assessment. Finally, the Shapley Additive Explanations (SHAP) method was used to investigate the…

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