Uma nova arquitetura para combinação de aglomerados espaciais e aplicação em epidemiologia

• Ano de defesa: 2015
• Autor(a) principal: Holmes, Danielly Cristina de Souza Costa.
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Ciências Exatas e da Saúde; Programa de Pós-Graduação em Modelos de Decisão e Saúde; UFPB
• Palavras-chave em Português: Métodos de aglomeração espaciais; Votação por maioria; Redes neurais; Combinação de classificadores; Spatial clustering methods; Majority voting; Neural networks; CIENCIAS DA SAUDE::SAUDE COLETIVA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/9016

The combination of classifiers aims to produce more accurate results to the decision-making process. Therefore, this study had the objective of proposing a new architecture based on a combination of spatial clustering methods and a more detailed voting map on the amount of votes that each geo-object received, applied to epidemiology. The methods of spatial clustering, in general, aim to identify the significant and not significant spatial clusters according to the study area. They are combined by combination of rules. In this work, the following rules were used: majority voting and neural networks. The new proposed architecture was applied to dengue data in the state of Paraiba, in the period from 2009 to 2011. According to the World Health Organization, dengue is a disease that annually records an average of 50 to 100 million cases worldwide, generating large financial burden on the health sector. A new architecture is proposed for the combination of the methods of spatial clustering. The combination of spatial clustering methods was applied in three case studies. In all three case studies, the new architecture identified more precisely the priority and nonpriority municipalities in Paraiba with regards to the dengue. In the case study 1, the combination rule was majority voting, in case study 2 the combination rule was neural networks and in case study 3 a new detailed voting map was proposed, identifying the amount of votes that each municipality had received. Analyzing the results from a spatial point of view, it was observed that the mesoregion called Sertão in the state of Paraiba had a greater number of priority municipalities; and the mesoregion of the Coast in Paraiba, the lowest number of priority municipalities. Regarding the research from the epidemiological point of view, it was observed that from the results of diagnostic tests (sensitivity, specificity, positive predictive value and negative predictive value) and the Kappa statistic, the combination of models produced satisfactory results. Concluding the analysis from the point of view of the combination of spatial clustering methods, it was observed that the new architecture presented satisfactory results by using the combination of the combination of rules. These results, from the epidemiological point of view, can assist managers in the decision-making process by verifying more precisely the regions that deserve special attention in combating the disease.