Fig. 1

a Graph showing how principal component analysis (PCA) can be used to approximate the distribution of the variable mean keratometry expected (KM_R) as a function of variables flatter keratometry (K1) and axial length (AL). Consecutive patients are marked in blue, while PCA approximation is in black. b Graph showing how the distance function in the three-dimensional space is used to approximate the distribution of the variable mean keratometry expected (KM_R) as a function of variables flatter keratometry (K1) and axial length (AL). Consecutive patients are marked in red, while the approximation area is in green. c Graph of changes in δs_KM for 403 consecutive patients and 7 different polynomial degrees. It is possible to observe that the error values are quite large and vary widely. Of the seven polynomial degrees, n = 7 was chosen (for example degree of polynomial 1 is red, degree of polynomial 2 is green etc.). d Graph of changes in mean keratometry expected (KM_R) and mean keratometry predicted (KM_S) for 403 individual patients (arranged in order from the highest values of KM_R)