Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark

Table 1

The features’ description of the database.

#	Feature	Abbreviation	Description
1	radius_mean	ra_mean	Mean of distances from the center to points on the perimeter
2	texture_mean	te_mean	Standard deviation of grayscale values
3	smoothness_mean	sm_mean	Mean of local variation in radius lengths
4	compactness_mean	com_mean	Mean of local variation in radius lengths
5	concavity_mean	con_mean	Mean of severity of concave portions of the contour
6	symmetry_mean	sy_mean	—
7	fractal_dimension_mean	fr_di_mean	Mean for “coastline approximation”-1
8	radius_se	ra_se	Standard error for the mean of distances from the center to points on the perimeter
9	texture_se	te_se	Standard error for the standard deviation of grayscale values
10	smoothness_se	sm_se	Standard error for local variation in radius lengths
11	compactness_se	com_se	Standard error for perimeter^2/area-1.0
12	concavity_se	con_se	Standard error for severity of concave portions of the contour
13	concave_points_se	con_po_se	Standard error for the number of concave portions of the contour
14	symmetry_se	sy_se	—
15	fractal_dimension_se	fr_di_se	Standard error for “coastline approximation”-1
16	smoothness_worst	sm_worst	“Worst” or largest mean value for local variation in radius lengths
17	compactness_worst	com_worst	“Worst” or largest mean value for perimeter^2/area-1.0
18	concavity_worst	con_worst	“Worst” or largest mean value for severity of concave portions of the contour
19	symmetry_worst	sym_worst	—
20	fractal_dimension_worst	fra_dim_worst	“Worst” or largest mean value for “coastline approximation”-1