Position Papers of the 18th Conference on Computer Science and Intelligence Systems

DOI: http://dx.doi.org/10.15439/2023F8583

Citation: Position Papers of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 36, pages 87–95 (2023)

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Abstract. In this study, our aim is to analyze the public health services in the city of Bogota, Colombia. We used unsupervised learning algorithms for clustering requests, complaints, claims, and denunciations issued to Supersalud in 2021. We collected the data from Supersalud's databases. We adopted clustering algorithms such as K-Means, Bisecting K-Means, and Gaussian Mixture, thus, we evaluated the quality of the combination using the silhouette coefficient. The algorithm with the best clustering quality to generate the clusters has been improved. Of the eight clusters, the first two present the highest incidences, with 181 and 249 affiliates affected for every 2,000 in the year 2021. In the first cluster, with 55\% support and 100\% confidence, a strong association was found between problems related to medical care facilities and restricted access to health services. In addition, in these two clusters RCCD with pathologies such as chronic communicable and non-communicable diseases (respiratory, diabetes, renal, risk factors and cardiovascular) associated with restricted access to health services were found. In conclusion, The unsupervised grouping allowed to analyze the public health services from the perspective of the RCCD, providing valuable information on the experiences of the users and the challenges in the provision of health services in Bogot\'a, these findings demonstrate the restriction in the access to health services from different perspectives of a deficient state regarding the provision of health services in the city of Bogot\'a, Colombia.

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