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Evaluation of Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer

  • Kour, Haneet (Department of Computer Science and IT, University of Jammu) ;
  • Manhas, Jatinder (Department of Computer Science & IT,Bhaderwah Campus, University of Jammu) ;
  • Sharma, Vinod (Department of Computer Science and IT, University of Jammu)
  • Received : 2019.04.30
  • Accepted : 2019.05.26
  • Published : 2019.06.30

Abstract

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

Keywords

AD;ANFIS;Fuzzy C-Means;Subtractive Clustering

References

  1. 2017 Alzheimer's Statistics, https://www.alzheimers.net/resources/alzheimers-statistics/, 2018.
  2. Cost and projections in the UK and globally, https://www.Dementiastatistics.org/statistics/cst-and-projections-in-the-uk-and-globally/,2018.
  3. M. F. Othman and T. M. Yau, "Neuro Fuzzy Classification and Detection Technique for Bioinformatics Problems", in Proceedings of the IEEE First Asia International Conference on Modelling & Simulation, 2007
  4. K. Arthi and A. Tamilarasi, "Prediction of autistic disorder using neuro-fuzzy system by applying ANN technique", International Journal of Developmental Neuroscience, vol. 26, no. 7, pp. 699-704, 2008. https://doi.org/10.1016/j.ijdevneu.2008.07.013
  5. J. C. Obi and A. A. Imainvan, "Decision Support System for the Intelligent Identification of Alzheimer using Neuro Fuzzy logic", International Journal on Soft Computing, vol. 2, no. 2, pp. 25-38, 2011.
  6. R. Sampath and A. Saradha, "Alzheimer's Disease Classification Using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) Classifier", Research Journal of Applied Sciences, Engineering and Technology, vol. 10, no. 1, pp. 29-34, 2015. https://doi.org/10.19026/rjaset.10.2550
  7. K. Ashish, A. Dasari, S. Chattopadhyay and N. B.Hui, "Genetic-neuro-fuzzy system for grading depression", Applied Computing and Informatics, vol. 14, no. 1, pp. 98-105, 2018. https://doi.org/10.1016/j.aci.2017.05.005
  8. L. Andrius, M. R.Maskeliūnas and D. Robertas, "ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms", Hindawi Journal of Healthcare Engineering, Vol. 1, 2018.
  9. Detecting Early Alzheimer's using MRI Data and Machine Learning,https://www.kaggle.com, 2019.
  10. M. M. Gupta, "Fuzzy Logic and Neural Networks", IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 636-639, 1992.
  11. Subtractive Clustering, https://www.mathworks.com/help/fuzzy/subclust.html, 2019 [online].
  12. FCM,http://www.mathworks.in/help/fuzzy/fcm.html, 2019 [online].