Artificial Neural Networks for the Diagnosis of Aggressive Periodontitis Trained by Immunologic Parameters
Figure 2
Univariate kernel density estimation (KDE) graphs.
Graphs A to C. Univariate KDE for radiographic bone loss measurements: modes (single, bimodal or multimodal) are defined as the values that appear more frequent. Graphs A & B from sample-1. In graph C (sample-2) we log transformed the confined data to find support in the interval (−∞, +∞) (see text S1). Graphs D to X. Univariate KDE for immunologic data: possible evidence of multimodality for the CD4/CD8 ratio, CD3, lymphocytes, monocytes, eosinophils, basophils and neutrophils counts (sample-2), IgG levels (sample-3), IL-2, IL-4, IL-6, INF-γ, TNF-α, IgG A.a. titers and IgG C.o. titers (sample-4). Mini clusters close to each other are detected for IL-1 and IgG P.g titers (sample-4).