Uma abordagem de descoberta de conhecimento para o suporte à gestão municipal de saúde

Abstract

The computational tools to support public policies, which allow the population and managers to assess the efficiency and effectiveness of expenditures, are increasingly relevant. In the healthcare area, thousands of assistances are provided daily, generating a large volume of data, requiring the help of appropriate tools to obtain knowledge of the data. One way to extract knowledge from this vast amount of data is through KDD which allows the identification of new useful, valid and understandable information. In the area of public health, the KDD can identify useful information to support municipal health management. This work analyzes the E-health base of the medical care profile in the municipal health units of Curitiba through the application of KDD, with WEKA tool and the Apriori algorithm in the data mining stage, to identify information about the care and profile of patients to assist with management. The results obtained are promising with the 34 rules found in mining, which provide information on care, specialties of doctors and distribution of care in the units according to the age group of patients.