Visualization models can assist in understanding the complex pattern of disease, where the signs may be buried in complex data. In this work we propose a new method for visualization of data derived from Heart Rate Variability (HRV) analysis, to indicate whether a person has developed, or is developing, signs of definite Cardiac Autonomic Neuropathy (CAN). Here, the visualizations are compared with actual data recorded from people attending a diabetes clinic with and without definite CAN. Indications from the new visualization technique are compared to the results of established diagnostic measures using the Ewing battery of tests. We find the proposed method to offer useful insights into this disease, as rather than relying upon a binary yes/no decision, it offers a comprehensive picture of the complexity of this disease.