EXperimental results using Self Organizing Maps (SOM) also called Kohonen neural \nnetworks for analyzing systolic hypertension using clinical and bio clinicalparameters. \nWe have conducted experiments using Sompy library available as open source in Python on 900 diabetes patients and concluded this particular type of artificial neural networks could learn patterns regarding correlation of systolic hypertension with different other clinical or bioclincal values, which we consider surprising due to \nthe fact self-organizing detects domain knowledge in data. \n
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