Abstract
Chronic pelvic pain is a common clinical condition with negative consequences in many aspects of womens life. The clinical presentation is heterogeneous and the involvement of several body systems impairs the identification of the exact etiology of the problem. At the same time, a clinical treatment of good quality depends on the professional and the learning process is slow. The goal of the paper is to show techniques used to create an artificial intelligence system capable of indicating the probable causes of this condition in order to help the doctors in the diagnosing process. This system uses a supervised learning algorithm along with multi-label problem modeling techniques and attribute selection algorithms to achieve the desired goal.
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Oliverio, V., Bendicto Poli-Neto, O. (2016). Artificial Intelligence Applied in the Multi-label Problem of Chronic Pelvic Pain Diagnosing. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science(), vol 10069. Springer, Cham. https://doi.org/10.1007/978-3-319-48746-5_9
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DOI: https://doi.org/10.1007/978-3-319-48746-5_9
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