  A lot of time is spent searching for the most performing data mining
algorithms applied in clinical diagnosis. The study set out to identify the
most performing predictive data mining algorithms applied in the diagnosis of
Erythemato-squamous diseases. The study used Naive Bayes, Multilayer Perceptron
and J48 decision tree induction to build predictive data mining models on 366
instances of Erythemato-squamous diseases datasets. Also, 10-fold
cross-validation and sets of performance metrics were used to evaluate the
baseline predictive performance of the classifiers. The comparative analysis
shows that the Naive Bayes performed best with accuracy of 97.4%, Multilayer
Perceptron came out second with accuracy of 96.6%, and J48 came out the worst
with accuracy of 93.5%. The evaluation of these classifiers on clinical
datasets, gave an insight into the predictive ability of different data mining
algorithms applicable in clinical diagnosis especially in the diagnosis of
Erythemato-squamous diseases.
