New Approach to Breast Cancer CAD using Partial Least Squares and Kernel-Partial Least Squares

摘要

Breast cancer is second only to lung cancer as a tumor-related cause of death in women. Currently, the method of choicefor the early detection of breast cancer is mammography. While sensitive to the detection of breast cancer, its positivepredictive value (PPV) is low, resulting in biopsies that are only 15-34% likely to reveal malignancy. This paperexplores the use of two novel approaches called Partial Least Squares (PLS) and Kernel-PLS (K-PLS) to the diagnosis ofbreast cancer. The approach is based on optimization for the partial least squares (PLS) algorithm for linear regressionand the K-PLS algorithm for non-linear regression. Preliminary results show that both the PLS and K-PLS paradigmsachieved comparable results with three separate support vector learning machines (SVLMs), where these SVLMs wereknown to have been trained to a global minimum. That is, the average performance of the three separate SVLMs wereAz = 0.9167927, with an average partial Az (Az90) = 0.5684283. These results compare favorably with the K-PLSparadigm, which obtained an Az = 0.907 and partial Az = 0.6123. The PLS paradigm provided comparable results.Secondly, both the K-PLS and PLS paradigms out performed the ANN in that the Az index improved by about 14%(Az ≅ 0.907 compared to the ANN Az of ≅ 0.8). The “Press R squared” value for the PLS and K-PLS machine learningalgorithms were 0.89 and 0.9, respectively, which is in good agreement with the other MOP values.