TY - GEN
T1 - Storytelling to Visualize Changes in Regions Based on Social Inclusion Indicators
AU - Pozos, Ernesto Emiliano Saucedo
AU - Luna, Gilberto Lorenzo Martínez
AU - Arenas, Adolfo Guzmán
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This paper shows an application of data science in the healthcare system by using the Social Inclusion Indicators (ISS) of each entity in Mexico for 25 years to make a clustering based on the lack of primary healthcare. Multiple procedures were applied, like cleaning and transformation of open data published by the Mexican Health Department, the imputation of missing values. With the complete information, data was scaled, and then one of the most common clustering algorithms was applied, which is K-Means. This algorithm was initialized with previously defined centroids to make it more standardized and make it easier to notice changes amongst the classes through the years. Six clusters were defined using previous works. All the implementations were made in Python using the Scikit-Learn library to apply the algorithms and measure performance, like K-Means and Mean Squared Error respectively. Results obtained were displayed using Tableau to observe in a more interactive way, how the classes had changed over the years.
AB - This paper shows an application of data science in the healthcare system by using the Social Inclusion Indicators (ISS) of each entity in Mexico for 25 years to make a clustering based on the lack of primary healthcare. Multiple procedures were applied, like cleaning and transformation of open data published by the Mexican Health Department, the imputation of missing values. With the complete information, data was scaled, and then one of the most common clustering algorithms was applied, which is K-Means. This algorithm was initialized with previously defined centroids to make it more standardized and make it easier to notice changes amongst the classes through the years. Six clusters were defined using previous works. All the implementations were made in Python using the Scikit-Learn library to apply the algorithms and measure performance, like K-Means and Mean Squared Error respectively. Results obtained were displayed using Tableau to observe in a more interactive way, how the classes had changed over the years.
KW - Clustering & visualization
KW - Imputation
KW - Indicators
UR - http://www.scopus.com/inward/record.url?scp=85142714129&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18082-8_11
DO - 10.1007/978-3-031-18082-8_11
M3 - Contribución a la conferencia
AN - SCOPUS:85142714129
SN - 9783031180811
T3 - Communications in Computer and Information Science
SP - 173
EP - 188
BT - Telematics and Computing - 11th International Congress, WITCOM 2022, Proceedings
A2 - Mata-Rivera, Miguel Félix
A2 - Zagal-Flores, Roberto
A2 - Barria-Huidobro, Cristian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th International Congress of Telematics and Computing, WITCOM 2022
Y2 - 7 November 2022 through 11 November 2022
ER -