Descoberta de conhecimento nas bases de dados da pandemia da COVID- 19 e de indicadores socioeconômicos e ambientais

Detalhes bibliográficos
Ano de defesa: 2023
Autor(a) principal: SOUZA, Lucelia Lima lattes
Orientador(a): BORCHARTT, Tiago Bonini lattes
Banca de defesa: BORCHARTT, Tiago Bonini lattes, COUTINHO, Luciano Reis lattes, CARVALHO, Sérgio Teixeira de lattes
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal do Maranhão
Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO/CCET
Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
País: Brasil
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/4731
Resumo: The COVID-19 pandemic has triggered a global public health crisis and required large- scale data analysis to better understand its spread and impact on society. In this context, “Knowledge Discovery in Databases” (KDD) is a useful tool, as it presents a well-defined methodology, with validated steps in different applications. The present work aims at discoveries of knowledge of data between COVID-19 and Socioeconomic and Environmental Indicators, through the use of Data Mining (DM) techniques - Data Mining, classifying new patterns with the KDD method, aiming to obtain the technique with the highest percentage of hits. For the problem under study, the KDD method used is composed of the steps of: selection, pre-processing, transformation, data mining and evaluation. Good results were obtained with the application of descriptive data mining methods, which involve correlation, grouping and association rule models, these were the techniques that stood out the most, with satisfactory generalization capabilities. The results of knowledge discovery in data from the COVID-19 pandemic can contribute to public policy formulation and computerized decision making in public health.