Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach

In this paper, a Fuzzy Association Rule Mining (FARM) with expert-driven approach is proposed to acquire a knowledge-base, which corresponds more intuitively to human perception with a high comprehensibility. This approach reduces the number of rules in the knowledge-base when compared with the Standard Rule-base Formulation (SRF) and makes possible the rating of the rules according to their relevance. The rule relevance is determined by the measures of significance and certainty factors. The approach is validated using a medical database and the result shows that this approach ultimately reduces the number of rules and enhances the comprehensibility of the expert system.

[1]  Idris Bharanidharan Shanmugam,et al.  Hybrid intelligent Intrusion Detection System , 2005 .

[2]  Christoph S. Herrmann,et al.  A Hybrid Fuzzy-Neural Expert System for Diagnosis , 1995, IJCAI.

[3]  Rajkumar Roy,et al.  DEVELOPMENT OF FUZZY EXPERT SYSTEM FOR CUSTOMER AND SERVICE ADVISOR CATEGORISATION WITHIN CONTACT CENTRE ENVIRONMENT , 2006 .

[4]  Martine De Cock,et al.  Fuzzy versus quantitative association rules: a fair data-driven comparison , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Payman Moallem,et al.  A Novel Fuzzy-Neural Based Medical Diagnosis System , 2008 .

[6]  Novruz Allahverdi,et al.  Design of a fuzzy expert system for determination of coronary heart disease risk , 2007, CompSysTech '07.

[7]  Charles K. Ayo,et al.  On Sharp Boundary Problem in Rule Based Expert Systems in the Medical Domain , 2010, Int. J. Heal. Inf. Syst. Informatics.

[8]  Man Hon Wong,et al.  Mining fuzzy association rules in databases , 1998, SGMD.

[9]  Attila Gyenesei,et al.  A Fuzzy Approach for Mining Quantitative Association Rules , 2000, Acta Cybern..

[10]  Ludmil Mikhailov,et al.  An interpretable fuzzy rule-based classification methodology for medical diagnosis , 2009, Artif. Intell. Medicine.

[11]  Harleen Kaur,et al.  Empirical Study on Applications of Data Mining Techniques in Healthcare , 2006 .

[12]  Ioannis Hatzilygeroudis,et al.  Fuzzy-Evolutionary Synergism in an Intelligent Medical Diagnosis System , 2006, KES.

[13]  Milindkumar V. Sarode,et al.  A fuzzy expert system design for diagnosis of cancer , 2010, International Conference on Digital Image Processing.

[14]  D. Levy,et al.  Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.

[15]  Ajith Abraham,et al.  130: Rule-based Expert Systems , 2005 .

[16]  Oladipupo O. Olufunke,et al.  On Sharp Boundary Problem in Rule Based Expert Systems in the Medical Domain , 2010 .

[17]  Shusaku Tsumoto,et al.  Evaluation of rule interestingness measures in medical knowledge discovery in databases , 2007, Artif. Intell. Medicine.

[18]  Ajith Abraham,et al.  Rule-Based Expert Systems , 2005 .

[19]  Novruz Allahverdi,et al.  A fuzzy expert system design for diagnosis of prostate cancer , 2003, CompSysTech '03.

[20]  Novruz ALLAHVERDI,et al.  International Conference on Computer Systems and Technologies-CompSysTech ’ 07 DESIGN OF A FUZZY EXPERT SYSTEM FOR DETERMINATION OF CORONARY HEART DISEASE RISK , 2007 .

[21]  Phayung Meesad.  Quantitative Measures of a Fuzzy Expert System , 2001 .

[22]  Magne Setnes,et al.  Compact and transparent fuzzy models and classifiers through iterative complexity reduction , 2001, IEEE Trans. Fuzzy Syst..