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)


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.


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


  1. 1.
    Güvenir, H., Demiröz, G., İlter, N.: Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif. Intell. Med. 13, 147–165 (1998)CrossRefGoogle Scholar
  2. 2.
    López, B., Plaza, E.: Case-based learning of plans and goal states in medical diagnosis. Artif. Intell. Med. 9, 29–60 (1997)CrossRefGoogle Scholar
  3. 3.
    Forsström, J., Eklund, P., Virtanen, H., Waxlax, J., Lähdevirta, J.: DIAGAID: a connectionist approach to determine the diagnostic value of clinical data. Artif. Intell. Med. 3, 193–201 (1991)CrossRefGoogle Scholar
  4. 4.
    Akkus¸ A., Guvenir, H.A.: K nearest neighbor classification on feature projections. In: Proceedings of ICML 1996, pp. 12–19 (1995)Google Scholar
  5. 5.
    Guvenir, H., Sirin, I.: Classification by feature partitioning. Mach. Learn. 23, 47–67 (1996)Google Scholar
  6. 6.
    Subhi Al-batah, M., Mat Isa, N., Klaib, M., Al-Betar, M.: Multiple adaptive neuro-fuzzy inference system with automatic features extraction algorithm for cervical cancer recognition. Comput. Math. Methods Med. 2014, 1–12 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, D., He, T., Li, Z., Cao, L., Dey, N., Ashour, A., Balas, V., McCauley, P., Lin, Y., Xu, J., Shi, F.: Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system. Neural Comput. Appl. 29, 1087–1102 (2016)CrossRefGoogle Scholar
  8. 8.
    Ahmed, S., Dey, N., Ashour, A., Sifaki-Pistolla, D., Bălas-Timar, D., Balas, V., Tavares, J.: Effect of fuzzy partitioning in Crohn’s disease classification: a neuro-fuzzy-based approach. Med. Biol. Eng. Comput. 55, 101–115 (2016)CrossRefGoogle Scholar
  9. 9.
    Samanta, S., Ahmed, S.S., Salem, M., Nath, S., Dey, N., Chowdhury, S.S.: Haralick features based automated glaucoma classification using back propagation neural network. In: Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing (FICTA), pp. 351–358 (2014)Google Scholar
  10. 10.
    Helwan, A., Uzun, D., Abiyev, R., Bush, J.: One-year survival prediction of myocardial infarction. Int. J. Adv. Comput. Sci. Appl. 8, 173–178 (2017)Google Scholar
  11. 11.
    Dey, N., Ashour, A., Beagum, S., Pistola, D., Gospodinov, M., Gospodinova, E., Tavares, J.: Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. J. Imaging 1, 60–84 (2015)CrossRefGoogle Scholar
  12. 12.
    Lu, J., Chang, Y., Ho, C.: The optimization of chiller loading by adaptive neuro-fuzzy inference system and genetic algorithms. Math. Probl. Eng. 2015, 1–10 (2015)Google Scholar
  13. 13.
    Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278–282 (1995)Google Scholar
  14. 14.
    Tin, K.H.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)CrossRefGoogle Scholar
  15. 15.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  16. 16.
    Archer, K., Kimes, R.: Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 2249–2260 (2008)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Breiman, L., Cutler, A.: Random forest (2005)Google Scholar
  18. 18.
    Horning, N.: Random forests: an algorithm for image classification and generation of continuous field data sets. In: International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS) 9–11 (2010)Google Scholar
  19. 19.
    Abiyev, R., Arslan, M., Gunsel, I., Cagman, A.: Robot pathfinding using vision based obstacle detection (2017)Google Scholar
  20. 20.
    Kohonen, T.: State of the art in neural computing. In: IEEE First International Conference on Neural Networks, vol. 1, pp. 79–90 (1987)Google Scholar
  21. 21.
    Idoko, J.B., Rahib, H.A., Mohammad, K.M.: Intelligent machine learning algorithms for colour segmentation. WSEAS Trans. Signal Process. 13, 232–240 (2017)Google Scholar
  22. 22.
    Bush, I., Abiyev, R., Sallam Ma’aitah, M., Altıparmak, H.: Integrated artificial intelligence algorithm for skin detection. In: ITM Web of Conferences, vol. 16, p. 02004 (2018)CrossRefGoogle Scholar
  23. 23.
    Khaleel, M., Abiyev, R., John, I.: Intelligent classification of liver disorder using fuzzy neural system. Int. J. Adv. Comput. Sci. Appl. 8, 25–31 (2017)Google Scholar
  24. 24.
    Rahib, A., Mohammad, M.K.S.: Deep convolutional neural networks for chest diseases detection. J. Healthc. Eng. 2018 (2018)Google Scholar
  25. 25.
    Abiyev, R., Altunkaya, K.: Neural network based biometric personal identification with fast iris segmentation. Int. J. Control Autom. Syst. 7, 17–23 (2009)CrossRefGoogle Scholar
  26. 26.
    Abiyev, R., Abizade, S.: Diagnosing Parkinson’s diseases using fuzzy neural system. Comput. Math. Methods Med. 2016, 1–9 (2016)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Rahib, H.A., Kemal, K.: Adaptive Iris segmentation. In: Lecture Notes in Computer Sciences. Springer, CS Press (2009)Google Scholar
  28. 28.
    Rahib, A., Koray, A.: Personal iris recognition using neural networks. Int. J. Secur. Its Appl. 2(2), 41–50 (2008)Google Scholar
  29. 29.
    Rahib, A., Koray, A.: Neural network based biometric personal identification. LNCS, Springer, CS press (2007)Google Scholar
  30. 30.
    Kamil, D., Idoko, J.B.: Automated classification of fruits: pawpaw fruit as a case study. In: International Conference on Man–Machine Interactions, pp. 365–374. Springer, Cham (2017)Google Scholar
  31. 31.
    Bush, I., Dimililer, K.: Static and dynamic pedestrian detection algorithm for visual based driver assistive system. In: ITM Web of Conferences, vol. 9, p. 03002 (2017)CrossRefGoogle Scholar
  32. 32.
    Helwan, A., Idoko, J., Abiyev, R.: Machine learning techniques for classification of breast tissue. Procedia Comput. Sci. 120, 402–410 (2017)CrossRefGoogle Scholar

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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