https://www.selleckchem.com/pr....oducts/guanidine-thi
This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.
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