  Breast cancer is the most common cancer in women. Classification of
cancer/non-cancer patients with clinical records requires high sensitivity and
specificity for an acceptable diagnosis test. The state-of-the-art
classification model - Convolutional Neural Network (CNN), however, cannot be
used with clinical data that are represented in 1-D format. CNN has been
designed to work on a set of 2-D matrices whose elements show some correlation
with neighboring elements such as in image data. Conversely, the data examples
represented as a set of 1-D vectors -- apart from the time series data --
cannot be used with CNN, but with other classification models such as
Artificial Neural Networks or RandomForest. We have proposed some novel
preprocessing methods of data wrangling that transform a 1-D data vector, to a
2-D graphical image with appropriate correlations among the fields to be
processed on CNN. We tested our methods on Wisconsin Original Breast Cancer
(WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) datasets. To our knowledge,
this work is novel on non-image to image data transformation for the non-time
series data. The transformed data processed with CNN using VGGnet-16 shows
competitive results for the WBC dataset and outperforms other known methods for
the WDBC dataset.
