Abstract
Spirometric evaluation of pulmonary function plays a critical role in the diagnosis, differentiation and management of respiratory disorders. In spirometry, there is a requirement that a large database is to be analyzed by the physician for effective investigation. Hence, there is a need for automated evaluation of spirometric parameters to diagnose respiratory abnormalities in order to ease the work of the physician. In this work, a neuro fuzzy based Adaptive Neuro Fuzzy Inference System (ANFIS), Multiple ANFIS and Complex valued ANFIS models are employed in classifying the spirometric data. Four different membership functions which include triangular, trapezoidal, Gaussian and Gbell are employed in these classification models. Results show that all the models are capable of classifying respiratory abnormalities. Also, it is observed that CANFIS model with Gaussian membership function performs better than other models and achieved higher accuracy. This study seems to be clinically relevant as this could be useful for mass screening of respiratory diseases.
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References
Robin, C., Vicky, T., Gareth, W.: Impact of an Educational Intervention on the Quality of Spirometry Performance in a General Practice: an Audit. Primary Care Respiratory Journal 20(2), 210–213 (2011)
Thomas, L.P.: Benefits of and barriers to the widespread use of spirometry. Current Opinion in Pulmonary Medicine 11, 115–120 (2005)
Sujatha, C.M., Mahesh, V., Ramakrishnan, S.: Comparison of two ANN methods for classification of spirometer data. Measurement Science Review 8(3), 53–57 (2008)
Jude, H.D., Kezi, S.V.C., Anitha, J.: Application of Neuro-Fuzzy Model for MR Brain Tumor Image Classification. Biomedical Soft Computing and Human Sciences 16(1), 95–102 (2009)
Uncu, U.: Evaluation of Pulmonary Function Tests by Using Fuzzy Logic Theory. Journal of Medical Systems 34(3), 241–250 (2010)
Derom, E., Van Weel, C., Listro, G., Buffels, J., Schermer, T., Lammers, E., Wouter, E., Decramer, M.: Primary care spirometry. European Respiratory Journal 31(1), 197–203 (2008)
Hong, T.-P., Wu, C.-H.: An Improved Weighted Clustering Algorithm for Determination of Application Nodes in Heterogeneous Sensor Networks. Journal of Information Hiding and Multimedia Signal Processing 2(2), 173–184 (2011)
Lin, T.C., Huang, H.C., Liao, B.Y., Pan, J.S.: An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index. International Journal of Computer Sciences and Engineering Systems 1(4), 253–257 (2007)
David, P., Daryl, F., Jen, C., Alan, K., Frank, C.: Earlier diagnosis and earlier treatment of COPD in primary care. Primary Care Respiratory Journal 20(1), 15–22 (2011)
Abdurrahim, A., Serkan, K., Niyazi, K., Osman, N.U., Nilgun, A.: Diagnosis of Renal Failure Disease Using Adaptive Neuro-Fuzzy Inference System. Journal of Medical Systems 34(6), 1003–1009 (2010)
Loukas, Y.L.: Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. J. Med. Chem. 44, 2772–2783 (2001)
Cheng, H.W., Baw, J.L., Lawrence, S.H.W.: The Association Forecasting of 13 Variants Within Seven Asthma Susceptibility Genes on 3 Serum IgE Groups in Taiwanese Population by Integrating of Adaptive Neuro-fuzzy Inference System (ANFIS) and Classification Analysis Methods. Journal of Medical Systems (2010), doi:10.1007/s10916-010-9457-4
Sujatha, C.M., Ramakrishnan, S.: Prediction of Forced Expiratory Volume in Pulmonary function test using Radial basis Neural Networks and k-means Clustering. Journal of Medical Systems 33(5), 347–351 (2009)
Centor, R.M.: Signal Detectability: The use of ROC curves and their analysis. Medical Decision Making 11(2), 102–106 (1991)
Sujatha, C.M., Ramakrishnan, S.: Prediction of forced expiratory volume in normal and restrictive respiratory functions using spirometry and self-organizing map. Journal of Medical Engineering & Technology 33(7), 538–543 (2009)
Elif, D.U.: Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer. Journal of Medical System 33(5), 353–358 (2009)
Guler, N.: Application of adaptive neuro-fuzzy inference system for detection of electrocardiographic changes in patients with partial epilepsy using feature extraction. Expert Systems with Applications 27(3), 323–330 (2004)
Inan, G., Elif, D.U.: Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. Journal of Neuroscience Methods, NSM-3945 (2005)
Gomathi, V., Ramar, K., Santhiyaku, A.J.: Human Facial Expression Recognition using MANFIS Model. Proceedings of World Academy of Science Engineering and Technology 38, 338–342 (2009)
Chi-Bin, C., Cheng, C.J., Lee, E.S.: Neuro-Fuzzy and Genetic Algorithm in Multiple Response Optimization. PERGAMON Computers and Mathematics with Applications 44, 1503–1514 (2002)
Eiji, M., Kenichi, N.: Modular neural network-type CANFIS neuro-fuzzy modeling for multi-illumination color device characterization. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 4, pp. 2090–2095 (2001), 2001)
Alireza, M.-A., Mohammad-R, A.-T.: Complex-Valued Adaptive Neuro Fuzzy Inference System-CANFIS. In: Proceedings of the 2004 World Automation Congress and Fifth International Symposium on Soft Computing for Industry, Spain (2004)
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Asaithambi, M., Manoharan, S.C., Subramanian, S. (2012). Classification of Respiratory Abnormalities Using Adaptive Neuro Fuzzy Inference System. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28493-9_8
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DOI: https://doi.org/10.1007/978-3-642-28493-9_8
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