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- W4237402502 abstract "This article describes experimental results using Self Organizing Maps (SOM) also called Kohonen neuralnetworks for analyzing systolic hypertension using clinical and bio clinical parameters. We have conductedexperiments using Sompy library available as open source in Python on 900 diabetes patients andconcluded this particular type of artificial neural networks could learn patterns regarding correlation ofsystolic hypertension with different other clinical or bio clinical values, which we consider surprising due tothe fact self-organizing detects domain knowledge in data. The Sompy library is available at the followingpage: https://github.com/sevamoo/SOMPY. Our preliminary data shows that between various biochemicalparameters in newly discovered diabetic patients there are interesting associations by clustering which canindicate some pathogenic mechanism and some clues for prevention and treatment." @default.
- W4237402502 created "2022-05-12" @default.
- W4237402502 creator A5008467961 @default.
- W4237402502 date "2019-06-20" @default.
- W4237402502 modified "2023-10-16" @default.
- W4237402502 title "Self Organizing Maps for Diabetes Parameter Analysis" @default.
- W4237402502 doi "https://doi.org/10.31219/osf.io/eajms" @default.
- W4237402502 hasPublicationYear "2019" @default.
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