Performance Comparison of Selected Classification Algorithms Based on Fuzzy Soft Set for Medical Data

Medical data is heterogeneous in nature and associated with uncertainties. For that reason, data mining has been assisting physicians in decision making and to cope with the information overload. A considerable amount of literature has been available on medical data classification based on data mining techniques to automate or facilitating the delineation of images. However, from image formation to the final analysis, medical imaging is still facing challenges. New imaging procedures for classification could overcome the inefficiencies and provide more reliable information to the medical experts. Therefore, this paper assesses the performance of selected classification algorithms based on fuzzy soft set for classification of medical data. There are two concepts that underlie the classification in the fuzzy soft set theory namely: classification based on decision making problem and classification based on similarity between two fuzzy soft set. The selected algorithms are evaluated based on two criteria: accuracy and computational time. Moreover, the conducted experiments demonstrated the effectiveness of fuzzy soft set for medical data categorization.

[1]  Kemal Polat,et al.  Medical diagnosis of atherosclerosis from Carotid Artery Doppler Signals using principal component analysis (PCA), k-NN based weighting pre-processing and Artificial Immune Recognition System (AIRS) , 2008, J. Biomed. Informatics.

[2]  Moh'd Rasoul Al-Hadidi,et al.  Solving Mammography Problems of Breast Cancer Detection Using Artificial Neural Networks and Image Processing Techniques , 2012 .

[3]  Ajoy Kumar Ray,et al.  Texture Classification Using a Novel, Soft-Set Theory Based Classification Algorithm , 2006, ACCV.

[4]  Pabitra Kumar Maji,et al.  FUZZY SOFT SETS , 2001 .

[5]  Dongkyoo Shin,et al.  A Comparative Study of Medical Data Classification Methods Based on Decision Tree and Bagging Algorithms , 2009, 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing.

[6]  Jie Wang,et al.  Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[7]  Yong Yang,et al.  Erratum to "A note on soft sets, rough soft sets and fuzzy soft sets" [Appl. Soft Comput. 11 (2011) 3329-3332 , 2011, Appl. Soft Comput..

[8]  A. R. Roy,et al.  A fuzzy soft set theoretic approach to decision making problems , 2007 .

[9]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[10]  Bobby Schmidt,et al.  Fuzzy math , 2001 .

[11]  R. Ibrahim,et al.  Soft set theory for automatic classification of traditional pakistani musical instruments sounds , 2012, 2012 International Conference on Computer & Information Science (ICCIS).

[12]  Kate Smith-Miles,et al.  On learning algorithm selection for classification , 2006, Appl. Soft Comput..

[13]  D. Molodtsov.  Soft set theory—First results , 1999 .

[14]  Osmar R. Zaïane,et al.  Application of Data Mining Techniques for Medical Image Classification , 2001, MDM/KDD.

[15]  Sotiris B. Kotsiantis,et al.  Supervised Machine Learning: A Review of Classification Techniques , 2007, Informatica.

[16]  H. Pourghassem,et al.  Medical X-ray Images Classification Based on Shape Features and Bayesian Rule , 2012, 2012 Fourth International Conference on Computational Intelligence and Communication Networks.

[17]  Muhammad Irfan Ali,et al.  A note on soft sets, rough soft sets and fuzzy soft sets , 2011, Appl. Soft Comput..

[18]  A. R. Roy,et al.  An application of soft sets in a decision making problem , 2002 .

[19]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[20]  Mustafa Mat Deris,et al.  Similarity Approach on Fuzzy Soft Set Based Numerical Data Classification , 2011, ICSECS.

[21]  M. Madheswaran,et al.  An improved pre-processing technique with image mining approach for the medical image classification , 2010, 2010 Second International conference on Computing, Communication and Networking Technologies.