Detection of Chronic Malnutrition in Children Under Five Years of Age Applying Multivariate Techniques
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This article shows the design of five classifiers for the detection of chronic malnutrition in children under five years of age, for which a database was processed with five quantitative and 26 qualitative variables that were subjected to a dimension decrease employing a multiple correspondence analysis, subsequently, by using neural networks the first classifier was generated. The following three designs used deep neural network techniques, starting from a principal component analysis that determined an architecture with a hidden layer that houses 96. 3% of the cumulative variance, the difference between these classifiers lies in the technique that determined the number of neurons in the hidden layer. The last designed classifier employs logistic regression as a traditional statistical technique. The study ended with tests performed with the best-performing classifier that allowed for establishing the most influential variables.
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