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

• Autor(a) principal: SOUZA, Lucelia Lima
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFMA
• Texto Completo: https://tedebc.ufma.br/jspui/handle/tede/tede/4731
• topic: COVID-19;; indicadores socioeconômicos;; descoberta de conhecimento;; data mining;; dados ambientais.; COVID-19;; socioeconomic indicators;; knowledge discovery;; data mining;; environmental data.; Banco de Dados; Ciência da Computação

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.