2026-04-082026-04-082026-02-20Oliveira, Samila Raphaela de. Aplicação de modelagem computacional e inteligência artificial na classificação de risco de surtos de doenças infecciosas na cidade de Maceió, Alagoas / Samilla Raphaela de Oliveira, Victor Luan de Lima Lemos – Dados eletrônicos (1 arquivo : 3,1 MB). – 2026. Sistema requerido: Adobe Acrobat Reader. Modo de acesso: Internet. Orientação: Prof. Dr. Tarsis Marinho de Souza. Co-orientador: Profa. Dra.Cledja Karina Rolim da Silva. Trabalho de Conclusão de Curso (Bacharelado em Sistemas de Informação) – Instituto Federal de Alagoas, Campus Arapiraca, Arapiraca, 2026. 1. Análise de dados. 2. Doenças infecciosas. 3. Visualização de dados. 4. Aprendizado de máquina. 5. Saúde. I. Lemos, Victor Luan de Lima. II. Título..https://repositorio.ifal.edu.br/handle/123456789/1481Infectious diseases have historically represented a significant threat to populations worldwide and remain a relevant and priority challenge for public health, reinforcing the need for innovative strategies focused on prevention, surveillance, and control. In Brazil, despite the existence of national epidemiological surveillance systems, the occurrence of outbreaks and the reemergence of infectious diseases reveal limitations and vulnerabilities in the capacity to anticipate and respond to epidemic events. In this context, the early identification of risk patterns is essential to support timely public health actions. Within this scenario, models based on artificial intelligence have gained prominence due to their ability to identify complex patterns and support public health planning. These approaches have demonstrated performance comparable to or superior to traditional statistical methods, becoming essential tools for epidemic monitoring and control. Therefore, this study aims to analyze and compare computational models and machine learning algorithms applied to the classification of the risk of infectious disease outbreaks, using data from the Notifiable Diseases Information System (SINAN). The study seeks to contribute to the advancement of computational modeling in public health and to the strengthening of evidence-based strategies in epidemiological surveillance.ptAttribution-NonCommercial-NoDerivs 3.0 BrazilAnálise de DadosDoenças InfecciosasVisualização de DadosAprendizado de MáquinaSaúdeInfectious DiseasesData AnalysisData VisualizationHealthMachine LearningTrabalho de Conclusão de CursoCIENCIAS EXATAS E DA TERRA