Analysis and diagnosis of erythemato-squamous diseases using CHAID decision trees

Erythemato-squamous diseases (ESDs) are common skin diseases. They consist of six different categories: Psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris. They all share the clinical features of erythema and scaling with very little differences. Their automatic detection is a challenging problem as they have overlapping signs and symptoms. This study evaluates the performance of CHAID decision trees (DTs) for the analysis and diagnosis of ESDs. DTs are nonparametric methods with no priori assumptions about the space distribution with the ability to generate understandable classification rules. This property makes them very efficient tools for physicians and medical specialists to understand the data and inspect the knowledge behind. The Chi-Squared Automatic Interaction Detection (CHAID) decision tree model is a very fast model with the ability to build wider decision trees and to handle all kinds of input variables (features). The CHAID model has many successful achievements especially when used as an interpreter rather than a classifier. Due to the small number of samples, this study uses Chi-square test with the Likelihood Ratio (LR) to get robust results. Ensembles of bagged and boosted CHAIDs were introduced to improve the stability and the accuracy of the model, but on the expense of interpretability. This paper presents the experimental results of the application of CHAID decision trees and their bagged and boosted ensembles for the deferential diagnosis of ESD using both clinical and histopathological features. The prediction accuracies of these models are benchmarked against the Artificial Neural Network (ANN) in terms of statistical accuracy, specificity, sensitivity, precision, true positive rate, true negative rate and F-score. Experimental results showed that bagged ensemble outperforms other modeling algorithms.