BalanicĒ V., Dumitrache I., Caramihai M., Rae W., Herbst C.
Dept. of Automatic Control and System Engineering, University POLITEHNICA of Bucharest, Romania; Dept. of Medical Physics, University of the Free State (UFS), Bloemfontein, South Africa
BalanicĒ, V., Dept. of Automatic Control and System Engineering, University POLITEHNICA of Bucharest, Romania; Dumitrache, I., Dept. of Automatic Control and System Engineering, University POLITEHNICA of Bucharest, Romania; Caramihai, M., Dept. of Automatic Control and System Engineering, University POLITEHNICA of Bucharest, Romania; Rae, W., Dept. of Medical Physics, University of the Free State (UFS), Bloemfontein, South Africa; Herbst, C., Dept. of Medical Physics, University of the Free State (UFS), Bloemfontein, South Africa
The decision process for selecting the best-suited follow-up treatment for a suspected breast cancer case is strongly dependent upon the correct diagnosis and assessment of the breast cancer risk. Despite the latest technological developments, the methods and criteria used to quantify the characteristics of detected lesion, so as to define the developmental stage of the breast cancer, and thus to finally arrive at a reliable (most probable) risk estimate, are still subjective and poorly defined for many clinicians. The present paper introduces a set of fuzzy rules that can be used to process the relevant data from breast cancer cases in order to give a breast cancer risk prognosis which can be qualitatively compared to that of an expert.