Abstract
In recent years, due to advancement in medical technologies and its devices, a large volume of medical data is generated continuously from different sources at every moment. Analyzing these large volumes of medical data and correctly diagnosing the diseases are challenging tasks. Generally, these medical data contain uncertain, imprecise, and incomplete information that affects the performance of the classification model. In this paper, Linguistic Neuro-Fuzzy (LNF) model is used for the classification of diseases. First, this model uses a linguistic fuzzification process that computes the membership values of each feature to overcome the uncertainty issues. Second, these membership values are passed to the ANN-based model to predict the disease. The objective of this research work lies in the applications of this LNF model that predicts the diseases. The effectiveness of this model is tested and validated using six benchmark medical datasets. The performance of the LNF model is compared with ANN and observed that the LNF model outperforms than ANN to handle the uncertainty problem.
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References
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification, vol. 13 (1994)
Das, H., Jena, A.K., Nayak, J., Naik, B., Behera, H.S.: A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification. In: Computational intelligence in data mining-volume 2, pp. 461–471. Springer, New Delhi (2015)
Sahani, R., Rout, C., Badajena, J.C., Jena, A.K., Das, H.: Classification of intrusion detection using data mining techniques. In: Progress in computing, analytics and networking, pp. 753–764. Springer, Singapore (2018)
Das, H., Naik, B., Behera, H.S.: Classification of diabetes mellitus disease (DMD): a data mining (DM) approach. In: Progress in computing, analytics and networking, pp. 539–549. Springer, Singapore (2018)
Haykin, S.: Neural networks, vol. 2. Prentice Hall, New York (1994)
Zadeh, L. A.: Fuzzy sets, Inform. Control. 8, 338–353 (1965)
Meher, S.K.: Efficient pattern classification model with neuro-fuzzy networks. Soft. Comput. 21(12), 3317–3334 (2017)
Meher, S.K.: Explicit rough–fuzzy pattern classification model. Pattern Recogn. Lett. 36, 54–61 (2014)
Ubeyli, E.D.: Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Comput. Method. Programs Biomed. 93(3), 313–321 (2009)
Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Netw. 3(5), 683–697 (1992)
Azar, A.T.: Neuro-fuzzy feature selection approach based on linguistic hedges for medical diagnosis. Int. J. Model. Ident. Control 22(3), 195–206 (2014)
Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., Akbari, E.: A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. J. Infect. Public Health (2018)
Shihabudheen, K.V., Pillai, G.N.: Recent advances in neuro-fuzzy system: a survey. Knowl.-Based Syst. 152, 136–162 (2018)
Kar, S., Das, S., Ghosh, P.K.: Applications of neuro fuzzy systems: a brief review and future outline. Appl. Soft Comput. 15, 243–259 (2014)
Pattnaik, P.K., Rautaray, S.S., Das, H., Nayak, J.: Progress in computing, analytics and networking. In: Proceedings of ICCAN, 710 (2017)
Pradhan, C., Das, H., Naik, B., Dey, N.: Handbook of Research on Information Security in Biomedical Signal Processing, pp. 1–414. IGI Global, Hershey, PA (2018)
Dey, N., Ashour, A.S., Kalia, H., Goswami, R., Das, H.: Histopathological Image Analysis in Medical Decision Making, pp. 1–340. IGI Global, Hershey, PA. https://doi.org/10.4018/978-1-5225-6316-7 (2019)
Sahoo, A.K., Mallik, S., Pradhan, C., Mishra, B.S.P., Barik, R.K., Das, H.: Intelligence-based health recommendation system using big data analytics. In: Big Data Analytics for Intelligent Healthcare Management, pp. 227–246. Academic Press (2019)
Sahoo, A.K., Pradhan, C., Das, H.: Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In: Nature Inspired Computing for Data Science, pp. 201–212. Springer, Cham (2020)
Dey, N., Das, H., Naik, B., Behera, H.S. (eds.): Big Data Analytics for Intelligent Healthcare Management. Academic Press (2019)
Rout, M., Jena, A.K., Rout, J.K., Das, H.: Teaching–learning optimization based cascaded low-complexity neural network model for exchange rates forecasting. In: Smart Intelligent Computing and Applications, pp. 635–645. Springer, Singapore (2020)
Nayak, J., Naik, B., Jena, A.K., Barik, R.K., Das, H.: Nature inspired optimizations in cloud computing: applications and challenges. In: Cloud Computing for Optimization: Foundations, Applications, and Challenges, pp. 1–26. Springer, Cham (2018)
Das, A.K., Pradhan, M., Dash, A.K., Pradhan, C., Das, H.: A constructive machine translation system for English to Odia translation. In: 2018 International Conference on Communication and Signal Processing (ICCSP), pp. 0854–0857. IEEE (2018, April)
Bache, K., Lichman, M.: UCI machine learning repository [http://archive.ics.uci.edu/ml], University of California, School of Information and Computer Science, Irvine, CA (2013)
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Das, H., Naik, B., Behera, H.S. (2020). Disease Classification Using Linguistic Neuro-Fuzzy Model. In: Das, H., Pattnaik, P., Rautaray, S., Li, KC. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 1119. Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_5
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DOI: https://doi.org/10.1007/978-981-15-2414-1_5
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