Treatment tuberculosis retrieval using decision tree

Due to the large volume of data generated in healthcare organizations, the use of data mining techniques becomes essential for improving the quality of care, physician practices and disease management. However, expert knowledge is not based only on rules, but also on a mixture of knowledge and experiences. It is in this context that we set the involvement of data mining techniques and CBR to support medical decision making in order to optimize the time and benefit from the experience of experts. We propose a support system for medical decision-making based on CBR and data mining. This system allows, from a database of examples, engaging a method of Symbolic induction and Cellular Inference Engine (MIC) for the construction of a case retrieval model. To evaluate this new approach we have customized the platform jCOLIBRI with a real case base about the treatment of tuberculosis.