Analysis of anemia morbidity patterns using unsupervised algorithms: an approach based on data from national health facilities

Authors

  • Maicol Jainor Ramos Salinas Universidad Nacional Mayor de San Marcos Lima, Peru
  • Fernando Miguel Villegas Pancca Universidad Nacional Mayor de San Marcos Lima, Peru
  • Billy Bruce Cordova Chipa Universidad Nacional Mayor de San Marcos Lima, Peru
  • Sebastian Pedro Cano Quito Universidad Nacional Mayor de San Marcos Lima, Peru https://orcid.org/0009-0009-2558-1810
  • Pedro Martin Lezama Gonzales Universidad Nacional Mayor de San Marcos Lima, Peru

DOI:

https://doi.org/10.15381/risi.v16i2.25776

Keywords:

Anemia, Data mining, Clustering

Abstract

Anemia is a significant public health challenge in Lima, Peru, especially among vulnerable populations. The application of data mining algorithms and pattern analysis offers a new perspective to address this issue. By leveraging large datasets, data mining enables the discovery of hidden patterns and correlations. By combining these findings with pattern analysis algorithms, it is possible to develop models that identify patterns of morbidity related to anemia and key risk factors. This enables healthcare professionals to take preventive measures and provide early interventions to those at higher risk. By anticipating anemia morbidity, more effective preventive strategies can be implemented, achieving a decrease in this disease and giving people of Peru a higher quality of health.

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Published

2023-12-30

Issue

Vol. 16 No. 2 (2023)

Section

Original Research Articles

License

Copyright (c) 2023 Maicol Jainor Ramos Salinas, Pedro Martin Lezama Gonzales, Fernando Miguel Villegas Pancca, Billy Bruce Cordova Chipa, Sebastian Pedro Cano Quito

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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How to Cite

[1]
“Analysis of anemia morbidity patterns using unsupervised algorithms: an approach based on data from national health facilities”, Rev.Investig.sist.inform., vol. 16, no. 2, pp. 15–24, Dec. 2023, doi: 10.15381/risi.v16i2.25776.