Medical Data Classification Using Similarity Measure of Fuzzy Soft Set Based Distance Measure

Saima Anwar Lashari, Rosziati Ibrahim, Norhalina Senan

Abstract


Medical data classification plays a crucial role in many medical imaging applications by automating or facilitating the delineation of medical images. A considerable amount of literature has been published on medical images classification based on data mining techniques to develop intelligent medical decision support systems to help the physicians. This paper assesses the performance of a new classification algorithm using similarity measure fuzzy soft set based distance based for numerical medical datasets. The proposed modelling comprises of five phases explicitly: data acquisition, data pre-processing, data partitioning, classification using FussCyier and performance evaluation. The proposed classifier FussCyier is evaluated on five performance matrices’: accuracy, precision, recall, F-Micro and computational time. Experimental results indicate that the proposed classifier performed comparatively better with existing fuzzy soft classifiers.

Keywords


Medical Data Classification; Similarity Measure; Fuzzy Soft Set; Distance Measure;

Full Text:

PDF

References


W. Raghupathi, “Data mining in healthcare” Healthcare Informatics: Improving Efficiency through Technology, Analytics, and Management, 353-372, 2016.

I. Yoo, P. Alafaireet, M. Marinov, K. Pena-Hernandez, R. Gopidi, J. F Chang, and L. Hua, “Data mining in healthcare and biomedicine: a survey of the literature,” Journal of medical systems, 36(4), 2431-2448, 2012.

S. A. Lashari and R. Ibrahim, “Performance Comparison of Selected Classification Algorithms Based on Fuzzy Soft Set for Medical Data,” In Advanced Computer and Communication Engineering Technology(pp. 813-820), 2015. Springer International Publishing.

S. A. Begum and O. M. Devi, “Fuzzy algorithms for pattern recognition in medical diagnosis,” Assam University Journal of Science and Technology, 7(2), 1-12, 2011.

L. A. Zadeh, “Fuzzy sets,” Information and control, 8(3), 338-353, 1965.

S. A. Lashari, R. Ibrahim, & N. Senan, Soft set theory for automatic classification of traditional Pakistani musical instruments sounds. In Computer & Information Science (ICCIS), 2012 International Conference on (Vol. 1, pp. 94-99). IEEE.

N. Senan, R. Ibrahim, N. M. Nawi, & M. M. Mokji, (2009). Feature extraction for traditional malay musical instruments classification system. In Soft Computing and Pattern Recognition, 2009. SOCPAR'09. International Conference of (pp. 454-459). IEEE.

M. M. Mushrif, S. Sengupta and A. K. Ray, “Texture classification using a novel, soft-set theory based classification algorithm,” In Computer Vision–ACCV 2006 (pp. 246-254), 2006, Springer Berlin Heidelberg.

B. Handaga, T. Herawan and M. M. Deris, “FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory,” International Journal of Fuzzy System Applications (IJFSA), 2(4), 29-46, 2012.

S. A. Lashari, R. Ibrahim, N. Senan, I. T. R. Yanto and T Herawan, “Application of Wavelet De-noising Filters in Mammogram Images Classification Using Fuzzy Soft Set,” In International Conference on Soft Computing and Data Mining (pp. 529-537), 2016, Springer, Cham.

L. Baccour, A. M., Alim and R. I. John, “Some Notes on Fuzzy Similarity Measures and Application to Classification of Shapes, Recognition of Arabic Sentences and Mosaic.” IAENG International Journal of Computer Science, 41(2), 81-90, 2014.

P. Majumdar and S. K. Samanta, “Generalised fuzzy soft sets,” Computers & Mathematics with Applications, 59(4), 1425-1432, 2010.

N. Kalaiselvi and H. H. Inbarani, “Fuzzy Soft Set Based Classification for Gene Expression Data,” arXiv preprint arXiv:1301.1502, 2013.

A. Asuncion, & D. J. Newman, (2007). UCI Machine Learning Repository [http://www. ics. uci. edu/~ mlearn/MLRepository. html]. Irvine, CA: University of California. School of Information and Computer Science, 12.

P. C. Thirumal and N. Nagarajan, “Utilization of data mining techniques for diagnosis of diabetes mellitus-a case study,” ARPN Journal of Engineering and Applied Science, 10(1), 2015.

K .Polat,, S. Şahan , H. Kodaz & S. Güneş, “Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism,”. Expert Systems with Applications, 32(1), 172-183, 2007.

N. C. Long, P.Meesad & H. Unger, “A highly accurate firefly based algorithm for heart disease prediction,” Expert Systems with Applications, 42(21), 8221-8231, 2015.

W. L. Zuo, Z. Y. Wang, T. Liu & H. L. Chen, “ Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach” Biomedical Signal Processing and Control, 8(4), 364-373,2013.

Ghofrani, F., Helfroush, M. S., Danyali, H., & Kazemi, K. (2014). Improving the performance of machine learning algorithms using fuzzy-based features for medical x-ray image classification. Journal of Intelligent & Fuzzy Systems, 27(6), 3169-3180.

P. C. Thirumal and N. Nagarajan, “Utilization of data mining techniques for diagnosis of diabetes mellitus-a case study,” ARPN Journal of Engineering and Applied Science, 10(1), 2015.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131