SCUT sampling and classification algorithms to identify levels of child malnutrition
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Child malnutrition results in millions of deaths every year. This condition is a potential problem in Peruvian society, especially in the rural parts of the country. The consequences of malnutrition range from physical limitations to declining mental performance and productivity for the individual. Government initiatives contribute to decreasing the causes of this disorder; however, these efforts are focused on long term solutions. The need for a fast and reliable way to detect these cases early on still exists. This paper compares classification techniques to determine which one is the most appropriate to classify cases of malnutrition. Neural networks and decision trees are used in combination with different sampling techniques, such as SCUT, SMOTE, random oversampling, random undersampling, and Tomek links. The models produced using oversampling techniques achieved high accuracies. Further, the models produced by the SCUT algorithm achieved high accuracies, preserved the behavior of the data and allowed for better representations of minority classes. The multilayer perceptron model that used the SCUT sampling techniques was chosen as the best model.
How to citeBaraybar Huambo, J. & Gutiérrez Cárdenas, J. M. (2020). SCUT sampling and classification algorithms to identify levels of child malnutrition. En Communications in Computer and Information Science. 6th International Conference on Information Management and Big Data, 1070, 194-206. SIMBig 2019; Lima; Peru; 21 August 2019 through 23 August 2019; Code 239409. https://doi.org/10.1007/978-3-030-46140-9_19
Category / SubcategoryPendiente / Pendiente
xmlui.dri2xhtml.METS-1.0.item-journal6th International Conference on Information Management and Big Data
Indexado en Scopus
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