Evaluation of Predictive Data Mining Algorithms in Erythemato-Squamous Disease Diagnosis

TL;DR

This study compares the predictive performance of Naive Bayes, Multilayer Perceptron, and J48 decision tree algorithms in diagnosing Erythemato-squamous diseases, finding Naive Bayes most accurate.

Contribution

It provides a comparative analysis of data mining algorithms for clinical diagnosis of Erythemato-squamous diseases, highlighting the most effective method.

Findings

Naive Bayes achieved 97.4% accuracy

Multilayer Perceptron achieved 96.6% accuracy

J48 achieved 93.5% accuracy

Abstract

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.