Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics
Agnieszka Wosiak, Danuta Zakrzewska
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 235–240 (2014)
Abstract. In medical diagnostics there is a constant need of searching for new methods of attribute acquiring, but it is difficult to asses if these new features can support the existing ones and can be useful in medical inference. In the paper the methodology of discovering features which are less informative while considering independently, however meaningful for diagnosis making, is investigated. The proposed methodology can contribute to the better use of attributes, which has not been considered in the diagnostics process so far. The experimental study, which concerns arterial hypertension as one of the civilization diseases demanding early detection and improved treatment is presented. The experiments confirmed that additional attributes enable obtaining the diagnostic results comparable to the ones received by using the most obvious features.