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Intensive Investigation in Differential Diagnosis of Erythemato-Squamous Diseases

  • Idoko John BushEmail author
  • Murat Arslan
  • Rahib Abiyev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

Research in the field of dermatology shows that differential diagnosis of erythemato-squamous diseases is one of the challenges seeking attention and to contribute to this problem, we designed four novel machine learning models exploring; Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machines (SVM) and Fuzzy Neural Network (FNN) techniques to accurately recommend the best model to dermatologists when diagnosing patients with erythemato-squamous diseases. At the design stage, we considered a dataset characterizing the six classes of the disease. To reduce the training time, the input data was normalized and scaled in interval; 0–1. Furthermore, we implored 10-fold cross-validation where the original sample was randomly segmented into 10 equal sized subsamples. These 10 outcomes from the folds are then averagely computed and produce a single prediction. Total performance of each of the models as depicted in table one shows that FNN outperformed the other 3 models hence, recommended for the differential diagnoses of these six classes of the disease.

Keywords

Random forest Support vector machine Multilayer Perceptron Fuzzy Neural Network Dermatology Erythemato-squamous disease 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering, Applied Artificial Intelligence Research CentreNear East UniversityNorth CyprusTurkey

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