Research Article

[Retracted] Diagnosis of Breast Cancer Pathology on the Wisconsin Dataset with the Help of Data Mining Classification and Clustering Techniques

Table 1

Characteristics of the samples.

(i) Radius: radius of all cells are shown by the mean, standard deviation, and worst value
(ii) Texture: the mean, standard deviation, and worst value of the grayscale change rates of interior surfaces are shown in the table below.
(iii) Perimeter: the perimeters of the cells were measured for the mean, standard deviation, and worst value
(iv) Area: the mean, standard deviation, and worst-case value of the surface areas of the cells are all calculated and displayed
(v) SVMothness: The average, standard deviation, and worst value of the radius lengths of neighbouring cells are all displayed in the graph
(vi) Compactness: , standard deviation, and worst value
(vii) Concavity: the mean, standard deviation, and worst value of the indentations and protrusions around the cell are all displayed on this graph
(viii) Concave points: the mean, standard deviation, and worst value for the number of indentation and protrusion sites around the cell are all calculated using this data
(ix) Symmetry: the mean, standard deviation, and worst value of the change in ellipse shape of cells were calculated
(x) Fractal dimension: there are three values for this ratio: the mean, standard deviation, and worst value. There are three values for this ratio.