SCUT Sampling and Classification Algorithms to Identify Levels of Child Malnutrition

Juan Baraybar-Huambo, Juan Gutiérrez-Cárdenas

Research output: Chapter in Book/Report/Conference proceedingPaper (Conference contribution)

Abstract

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.

Original languageEnglish
Title of host publicationInformation Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
EditorsJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
PublisherSpringer
Pages194-206
Number of pages13
ISBN (Print)9783030461393
DOIs
StatePublished - 1 Jan 2020
Event6th International Conference on Information Management and Big Data, SIMBig 2019 - Lima, Peru
Duration: 21 Aug 201923 Aug 2019

Publication series

NameCommunications in Computer and Information Science
Volume1070 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference6th International Conference on Information Management and Big Data, SIMBig 2019
CountryPeru
CityLima
Period21/08/1923/08/19

Keywords

  • Child malnutrition
  • Decision trees
  • Neural networks
  • Random forest
  • Sampling techniques

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  • Cite this

    Baraybar-Huambo, J., & Gutiérrez-Cárdenas, J. (2020). SCUT Sampling and Classification Algorithms to Identify Levels of Child Malnutrition. In J. A. Lossio-Ventura, N. Condori-Fernandez, & J. C. Valverde-Rebaza (Eds.), Information Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings (pp. 194-206). (Communications in Computer and Information Science; Vol. 1070 CCIS). Springer. https://doi.org/10.1007/978-3-030-46140-9_19