Detecting Skin Diseases

Dermatitis

The UCI data set (Ilter and Altay Güvenir 1998) consist of

• \(366\) patients (\(8\) with missing values)
• \(12\) clinical attributes (erythema, itching, scaling, etc.)
• \(21\) histopathological attributes (decrease of melanin, spongiosis, etc.)

and a class variable with six different skin diseases

• psoriasis (\(111\) patients)
• seborrheic dermatitis (\(60\) patients)
• lichen planus (\(71\) patients)
• pityriasis rosea (\(48\) patients)
• chronic dermatitis (\(48\) patients)
• pityriasis rubra pilaris (\(20\) patients)

Many of the classical machine learning algorithms have been applied to the dataset (Liu et al. 2015). They all achieve a prediction accuracy above \(95\%\) and some even above \(99\%\). But…:

• The skin diseases share many common features
• We have exclusive classes but possibly not exhaustive
• What if a patient does not suffer from one of the six diseases
• Classification forces us to label a patient with one and only one disease

Goal

Given a new patient \(y\), we want to test the hypotheses

\[\begin{align*} H_1: & y \text{ has psoriasis} \\ H_2: & y \text{ has seborrheic dermatitis} \\ H_3: & y \text{ has lichen planus} \\ H_4: & y \text{ has pityriasis rosea} \\ H_5: & y \text{ has chronic dermatitis} \\ H_6: & y \text{ has pityriasis rubra pilaris} \end{align*}\]

Since all hypotheses are exclusive we do not correct for multiple hypothesis testing (but the user can do this by setting the significance level accordingly).

Modeling Attributes of Psoriasis

We first show how to test \(H_1\). First extract the psoriasis data:

library(dplyr)
library(molic)
y     <- unlist(derma[80, -35]) # a patient with seboreic dermatitis
psor  <- derma %>%
  filter(ES == "psoriasis") %>%
  dplyr::select(-ES)

Next, we fit the interaction graph for the psoriasis patients:

library(ess)
g <- fit_graph(psor, q = 0, trace = FALSE)

We can color the nodes corresponding to clinical attributes (red), histopathological attributes (green) and the age variable (gray):

vs   <- names(adj_lst(g))
vcol <- structure(vector("character", length(vs)), names = vs)
vcol[grepl("c", vs)] <- "tomato"  # clinical attributes
vcol[grepl("h", vs)] <- "#98FB98" # histopathological attributes
vcol["age"]          <- "gray"    # age variable

plot(g, vcol, vertex.size = 10, vertex.label = NA)

The take home message here is, that we cannot assume independence between the attributes for the psoriasis patient as seen in the interaction graph - there are many associations.

Outlier Model for Psoriasis Patients

set.seed(300718)
m <- fit_outlier(psor, g, y)
print(m)

 -------------------------------- 
  Simulations: 10000 
  Variables: 34 
  Observations: 112 
  Estimated mean: 42.74 
  Estimated variance: 37.73 
 --------------------------------
  Critical value: 53.70377 
  Deviance: 50.55186 
  P-value: 0.1054 
  Alpha: 0.05 
  <novelty, outlier_model, list> 
 --------------------------------

Notice that that the number of observations is \(112\) even though we have only observed \(111\) psoriasis patients. This is because, under the hypothesis, \(H_1\), the new observation \(y\) has psoriasis. The other summary statistics is self explanatory.

Plotting the Approximated Density of the Test Statistic

plot(m)

The red area is the critical region (here 5%) and the dotted line is the observed test statistic (the deviance) of \(y\). Since the dotted line is outside the critical region, we cannot reject that \(y\) has psoriasis.

Testing all Hypothesis Simultaneously

We can use the fit_multiple_models function to test all six hypothesis as follows.

set.seed(300718)
mm <- fit_multiple_models(derma, y, "ES", q = 0,trace = FALSE) 
plot(mm)

Thus, we cannot reject that \(y\) has either psoriasis, seboreic dermatitis or pityriasis rosea. This is conservative compared to classification methods and hence a little safer. The medical expert should proceed the investigation from here.