Data Mining to Determine Behavioral Patterns in Respiratory Disease in Pediatric Patients

There are several varieties of respiratory diseases which mainly affect children between 0 and 5 years of age, not having a complete report of the behavior of each of these. This research seeks to conduct a study of the behavior of patterns in respiratory diseases of children in Peru through data mining, using data generated by the health sector, organizations and research between the years 2015 to 2019. This process was given by means of the K-Means clustering algorithm which allowed performing an analysis of this data identifying the patterns in a total of 10,000 Peruvian clinical records between the years mentioned, generating different behaviors. Through the grouping obtained in the clusters, it was obtained as a result that most of the cases in all the ages studied, they presented diseases with codes between the range of 000 and 060 approximately. This research was carried out in order to help health centers in Peru for further study, documentation and due decision-making, waiting for optimal prevention strategies regarding respiratory diseases.

@article{Cabanillas-Carbonell2021,
  title     = {Data Mining to Determine Behavioral Patterns in Respiratory Disease in Pediatric Patients},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {7},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Michael Cabanillas-Carbonell and Randy Verdecia-Peña and José Luis Herrera Salazar and Esteban Medina-Rafaile and Oswaldo Casazola-Cruz},
  doi       = {10.14569/IJACSA.2021.0120749},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120749}
}