An Adaptive Soft Set Based Diagnostic Risk Prediction System

Recently, risk based prediction models in medical diagnostic systems gain wider significance in deciding most appropriate diagnostic treatments and for clinical usage. Prostate cancer is a disease which is difficult to diagnose and there are number of failure cases reported. Therefore, an effective and aggressive selection of multiple factors influence on the disease is required. In this paper, an adaptive soft set based diagnostic risk prediction system is presented with the implementation on prostate cancer. The system receives input parameters related to the disease and gives out the risk percentage of the patient. Soft sets are generated with the input parameters by fuzzification followed by rule generation. The risk percentage of the rules are individually calculated for Precision, Recall and F-Measure, that conclude on the best risk percentage based on the maximum area under the curve (AUC) in each case. This ensures to select the most influential risk parameters in treating the disease. Specificity and sensitivity of the test system yield 75.00% and 45.45% respectively.

[1]  Ajoy Kanti Das,et al.  Weighted fuzzy soft multiset and decision-making , 2016, International Journal of Machine Learning and Cybernetics.

[2]  Krzysztof Slowinski,et al.  Rough Classification of HSV Patients , 1992, Intelligent Decision Support.

[3]  Jianming Zhan,et al.  A novel soft rough set: Soft rough hemirings and corresponding multicriteria group decision making , 2017, Appl. Soft Comput..

[4]  Theodore E. Cohn,et al.  Receiver operating characteristic analysis of photoreceptor sensitivity , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Jianming Zhan,et al.  A survey of decision making methods based on certain hybrid soft set models , 2016, Artificial Intelligence Review.

[6]  Novruz Allahverdi,et al.  A fuzzy approach for determination of prostate cancer , 2013 .

[7]  Naime Tozlu,et al.  An application of multicriteria group decision making by soft covering based rough sets , 2015 .

[8]  Giampaolo E. D'Errico,et al.  Receiver operating characteristic: A tool for cell confluence estimation , 2015, 2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings.

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

[10]  Naim Çag ˘ man,et al.  Soft set theory and uni-int decision making , 2010 .

[11]  Saziye Yuksel,et al.  Application of soft sets to diagnose the prostate cancer risk , 2013, Journal of Inequalities and Applications.

[12]  Naim Çagman,et al.  Soft set theory and uni-int decision making , 2010, Eur. J. Oper. Res..

[13]  L. Benecchi,et al.  Neuro-fuzzy system for prostate cancer diagnosis. , 2006, Urology.

[14]  E. Sanchez,et al.  Inverses of fuzzy relations: Application to possibility distributions and medical diagnosis , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[15]  Xiaoyan Liu,et al.  On some new operations in soft set theory , 2009, Comput. Math. Appl..

[16]  Yıldıray Çelik,et al.  Fuzzy soft set theory applied to medical diagnosis using fuzzy arithmetic operations , 2013 .

[17]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[18]  José Carlos Rodriguez Alcantud,et al.  Hesitant Fuzzy Worth: An innovative ranking methodology for hesitant fuzzy subsets , 2016, Appl. Soft Comput..

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

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

[21]  V. Torra,et al.  A framework for linguistic logic programming , 2010 .

[22]  Feng Feng,et al.  Soft subsets and soft product operations , 2013, Inf. Sci..

[23]  Bijan Davvaz,et al.  Soft sets combined with fuzzy sets and rough sets: a tentative approach , 2010, Soft Comput..

[24]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

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

[26]  Young Bae Jun,et al.  Soft sets and soft rough sets , 2011, Inf. Sci..

[27]  A. R. Roy,et al.  Soft set theory , 2003 .

[28]  Charles X. Ling,et al.  Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[30]  Marek J. Druzdzel,et al.  Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems , 2013, Artif. Intell. Medicine.

[31]  H. E. Pople,et al.  Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine , 1982 .

[32]  José Carlos Rodriguez Alcantud,et al.  Glaucoma Diagnosis: A Soft Set Based Decision Making Procedure , 2015, CAEPIA.

[33]  Mignon Park,et al.  Developing a Knowledge-Based System to Automate the Diagnosis of Allergic Rhinitis , 1996 .

[34]  Yong Yang,et al.  Algorithms for interval-valued fuzzy soft sets in stochastic multi-criteria decision making based on regret theory and prospect theory with combined weight , 2017, Appl. Soft Comput..

[35]  Edward H. Shortliffe,et al.  Computer-based medical consultations, MYCIN , 1976 .

[36]  José Carlos Rodriguez Alcantud,et al.  Probabilistic soft sets and dual probabilistic soft sets in decision-making , 2017, Neural Computing and Applications.

[37]  Jerome P. Richie,et al.  Use of the percentage of free prostate-specific antigen to enhance differentiation of prostate cancer from benign prostatic disease: a prospective multicenter clinical trial. , 1998, JAMA.

[38]  Aytürk Keles,et al.  Neuro-fuzzy classification of prostate cancer using NEFCLASS-J , 2007, Comput. Biol. Medicine.

[39]  H. Joensuu,et al.  Needle biopsy through the abdominal wall for the diagnosis of gastrointestinal stromal tumour - Does it increase the risk for tumour cell seeding and recurrence? , 2016, European journal of cancer.