Navegando por Assunto "Data Science"

Agora exibindo 1 - 3 de 3

Acesso aberto (Open Access)
Ciência de Dados Aplicada em Dados Públicos: Estudos de Caso Acerca da Previdência Social Brasileira.
(Universidade Federal do Pará, 2020-04-17) FELIX JÚNIOR, Francisco Eguinaldo de Albuquerque; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497
Data Science is an interdisciplinary area related to data analysis, which aims to extract knowledge and possible decision-making about specific problems. In this context, open government data, which often need pre-treatments and computational methods to process their data sets, present themselves as potential sources of information to be explored taking the Data Science’s perspective, allowing the development of strategies each time more efficient and optimized in public management. Given this, and allied to the recent discussions related to the reform in the Brazilian social security, this dissertation presents two case studies referring to analyzes in the national social security system. The first study used the microdata referring to the demographic censuses of 2000 and 2010, made available by IBGE, proposing to evaluate the participation that retirements and pensions have in the income inequality of the population in the years evaluated about Brazilian states and municipalities. The results show that, although the analyzed benefits contribute to the Brazil income concentration, the portion corres=ponding to a minimum wage contributes to the deconcentration of income, and the portion above one salary contributes to the concentration, being a repetitive pattern throughout the country. On the other hand, the second study proposed an evaluation of the impacts caused by the pension reform, which is proposed in PEC 06/2019. It was observed that PEC 06/2019 would hinder access to benefits, in which approximately 83,28% of the pensions would not have been granted had it been in effect since 1995.
Acesso aberto (Open Access)
Ciência de dados e aprendizado de máquina aplicados ao estudo de variáveis epidemiológica hanseníase na Amazônia
(Universidade Federal do Pará, 2024-12-18) FALCÃO, Igor Wenner Silva; CARDOSO, Diego Lisboa; http://lattes.cnpq.br/0507944343674734; SERUFFO, Marcos César da Rocha; http://lattes.cnpq.br/3794198610723464
Leprosy is a significant public health problem that largely affects low-income populations. Although the World Health Organization (WHO) establishes guidelines for diagnosis, prevention, and treatment, disease detection faces limitations, often resulting in late or inaccurate diagnoses and leading to serious neurological complications and multidrug-resistant cases. Therefore, early diagnosis is essential to reduce the burden of this disease. Machine learning has been widely used in several areas of science and industry, but especially in health, where it plays an essential role in the analysis and treatment of large volumes of data. In this sense, this thesis investigates the application of a model based on Data Science and Machine Learning to act in the specification of the clinical profile of possible leprosy cases in the Amazon Region and, thus, to be able to act preventively in the early diagnosis and treatment of patients under medical followup. The work takes into account clinical data of patients from a non-public dataset, collected between 2015 and 2020 in the North region of Brazil. Therefore, this thesis proposes a learning model to identify groups clinically affected by the disease using Clustering and Random Forest techniques. In the results obtained, the proposed model demonstrated efficiency in evaluating the probability of individuals being ill, achieving an accuracy of 90.39% in the performance evaluation and identifying a probability of 83.46% of an individual being ill, considering a set of epidemiological and non-generic variables. This approach offers a promising vision for the future of health, allowing the formulation of effective strategies for the early identification of possible cases.
Acesso aberto (Open Access)
Data science aplicado a dados abertos do Governo Federal: estudos de caso sobre a economia dos municípios brasileiros.
(Universidade Federal do Pará, 2020-03-13) SANTOS, Sandio Maciel dos; SILVA, Marcelino Silva da; http://lattes.cnpq.br/7080513172499497
The process of analyzing open databases in recent years has gained considerable prominence in the Brazilian scenario since the granting of Law 12,527 / 2011, which guarantees access to public information, allowing for better transparency of public spending by society. Allied to this, numerous discussions arose around the use of Brazilian government microdata, among which we highlight the discussions on social security reform and the analysis of fiscal health in Brazilian municipalities through social security approaches. Thus, this work focuses on the use of Data Science, specifically in the KDD process to analyze microdata from Brazilian municipalities. Thus, in this work, two different approaches are made, the first of which performs a descriptive statistical analysis without inferences, to understand the fiscal health of Brazilian municipalities between 2010 and 2017, through transfers from the RGPS. The second approach to fiscal analysis using the STVAR model through the following variables: expenditure, revenue, and GDP of the municipality of São Paulo. The results of analysis I show that municipalities with populations greater than 100 inhabitants do not show a deficit due to the difference between municipal collections and transfers from the RGPS. In analysis II, the results found show that the economic cycle analyzed when undergoing exogenous shock (or external impulse) can generate changes in the states of recession and expansion with an average duration of 12 months.