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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Güvenir, H., Demiröz, G., İlter, N.: Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif. Intell. Med. 13, 147–165 (1998)
López, B., Plaza, E.: Case-based learning of plans and goal states in medical diagnosis. Artif. Intell. Med. 9, 29–60 (1997)
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)
Akkus¸ A., Guvenir, H.A.: K nearest neighbor classification on feature projections. In: Proceedings of ICML 1996, pp. 12–19 (1995)
Guvenir, H., Sirin, I.: Classification by feature partitioning. Mach. Learn. 23, 47–67 (1996)
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)
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)
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)
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)
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)
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)
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)
Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, pp. 278–282 (1995)
Tin, K.H.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Archer, K., Kimes, R.: Empirical characterization of random forest variable importance measures. Comput. Stat. Data Anal. 52, 2249–2260 (2008)
Breiman, L., Cutler, A.: Random forest (2005)
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)
Abiyev, R., Arslan, M., Gunsel, I., Cagman, A.: Robot pathfinding using vision based obstacle detection (2017)
Kohonen, T.: State of the art in neural computing. In: IEEE First International Conference on Neural Networks, vol. 1, pp. 79–90 (1987)
Idoko, J.B., Rahib, H.A., Mohammad, K.M.: Intelligent machine learning algorithms for colour segmentation. WSEAS Trans. Signal Process. 13, 232–240 (2017)
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)
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)
Rahib, A., Mohammad, M.K.S.: Deep convolutional neural networks for chest diseases detection. J. Healthc. Eng. 2018 (2018)
Abiyev, R., Altunkaya, K.: Neural network based biometric personal identification with fast iris segmentation. Int. J. Control Autom. Syst. 7, 17–23 (2009)
Abiyev, R., Abizade, S.: Diagnosing Parkinson’s diseases using fuzzy neural system. Comput. Math. Methods Med. 2016, 1–9 (2016)
Rahib, H.A., Kemal, K.: Adaptive Iris segmentation. In: Lecture Notes in Computer Sciences. Springer, CS Press (2009)
Rahib, A., Koray, A.: Personal iris recognition using neural networks. Int. J. Secur. Its Appl. 2(2), 41–50 (2008)
Rahib, A., Koray, A.: Neural network based biometric personal identification. LNCS, Springer, CS press (2007)
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)
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)
Helwan, A., Idoko, J., Abiyev, R.: Machine learning techniques for classification of breast tissue. Procedia Comput. Sci. 120, 402–410 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bush, I.J., Arslan, M., Abiyev, R. (2019). Intensive Investigation in Differential Diagnosis of Erythemato-Squamous Diseases. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Sadikoglu, F. (eds) 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018. ICAFS 2018. Advances in Intelligent Systems and Computing, vol 896. Springer, Cham. https://doi.org/10.1007/978-3-030-04164-9_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-04164-9_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04163-2
Online ISBN: 978-3-030-04164-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)