A Fuzzy Probabilistic Method for Medical Diagnosis

The max-min composition in fuzzy set theory has attained reasonable success in medical diagnosis in the past thirty years for estimating the probability of a patient diagnosed with a certain disease. However, there has been no theoretical justification why the method would work. We create a theoretical model to calculate the probabilities of hypothetical patients having designated diseases, and use simulated dataset to explain why the max-min composition has been successful. In addition, based on the theoretical model, we propose a fuzzy probabilistic method to estimate the probability of a patient having a certain disease. The proposed method may produce a more accurate estimate than the max-min composition.

[1]  Sukhamay Kundu The min-max composition rule and its superiority over the usual max-min composition rule , 1998, Fuzzy Sets Syst..

[2]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[3]  Kavishwar B. Wagholikar,et al.  Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions , 2012, Journal of Medical Systems.

[4]  E. Sanchez SOLUTIONS IN COMPOSITE FUZZY RELATION EQUATIONS: APPLICATION TO MEDICAL DIAGNOSIS IN BROUWERIAN LOGIC , 1993 .

[5]  H. Tanaka,et al.  A Formulation of Fuzzy Decision Problems with Fuzzy Information using Probability Measures of Fuzzy Events , 1978, Inf. Control..

[6]  Kavishwar B. Wagholikar,et al.  Evaluation of Fuzzy Relation Method for Medical Decision Support , 2012, Journal of Medical Systems.

[7]  Klaus-Peter Adlassnig,et al.  Fuzzy Set Theory in Medical Diagnosis , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[9]  Ranjit Biswas,et al.  An application of intuitionistic fuzzy sets in medical diagnosis , 2001, Fuzzy Sets Syst..

[10]  Irina Rish,et al.  An empirical study of the naive Bayes classifier , 2001 .

[11]  Kavishwar B. Wagholikar,et al.  Fuzzy naive bayesian model for medical diagnostic decision support , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Ahmad Mahir Razali,et al.  Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis , 2011 .

[13]  Michael Beer,et al.  A Summary on Fuzzy Probability Theory , 2010, 2010 IEEE International Conference on Granular Computing.

[14]  S. Bugajski,et al.  Fundamentals of fuzzy probability theory , 1996 .

[15]  Igor Kononenko,et al.  Inductive and Bayesian learning in medical diagnosis , 1993, Appl. Artif. Intell..

[16]  Michael Beer Fuzzy Probability Theory , 2009, Encyclopedia of Complexity and Systems Science.

[17]  Didier Sornette,et al.  Encyclopedia of Complexity and Systems Science , 2009 .

[18]  Rudolf Seising,et al.  From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis , 2006, Artif. Intell. Medicine.

[19]  Elie Sanchez,et al.  Resolution of Composite Fuzzy Relation Equations , 1976, Inf. Control..

[20]  Lotfi A. Zadeh,et al.  Similarity relations and fuzzy orderings , 1971, Inf. Sci..

[21]  Stan Gudder What Is Fuzzy Probability Theory? , 2000 .

[22]  C. Desoer,et al.  Linear System Theory , 1963 .

[23]  Dale Schuurmans,et al.  Augmenting Naive Bayes Classifiers with Statistical Language Models , 2004, Information Retrieval.

[24]  Kavishwar B. Wagholikar,et al.  Fuzzy relation based modeling for medical diagnostic decision support: Case studies , 2008, Int. J. Knowl. Based Intell. Eng. Syst..

[25]  Shubhajit Roy Chowdhury,et al.  Accuracy Enhancement in a Fuzzy Expert Decision Making System Through Appropriate Determination of Membership Functions and Its Application in a Medical Diagnostic Decision Making System , 2012, Journal of Medical Systems.

[26]  . S.P.Rajagopalan,et al.  Fuzzy Logic Approach for Diagnosis of Diabetics , 2007 .

[27]  M. Balamurugan,et al.  Fuzzy Max-Min Composition Technique in Medical Diagnosis , 2012 .

[28]  Mohammad Reza Daliri,et al.  A Hybrid Automatic System for the Diagnosis of Lung Cancer Based on Genetic Algorithm and Fuzzy Extreme Learning Machines , 2012, Journal of Medical Systems.

[29]  U. Rajendra Acharya,et al.  A Novel Mathematical Approach to Diagnose Premenstrual Syndrome , 2011, Journal of Medical Systems.

[30]  Lotfi A. Zadeh Toward a perception-based theory of probabilistic reasoning with imprecise probabilities , 2003 .

[31]  Tardi Tjahjadi,et al.  Cadosa: A fuzzy expert system for differential diagnosis of obstructive sleep apnoea and related conditions , 1997 .

[32]  Pedro M. Domingos,et al.  On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.