Prognosis of Liver Disorders in Dna Positive Hbv Patients Based on Fuzzy Soft Sets

Authors

  • Ramadhan A. Mohammed University of Duhok
  • Ahmed M. Salih University of Duhok
  • Muayad A. Mirza University of Mosul
  • Tahir H. Ismail University of Mosul
  • Ahmed A. Allam University of Assiut

DOI:

https://doi.org/10.25271/2017.5.1.311

Keywords:

Fuzzy soft expert system, Liver disorders disease, Mean corpuscular volume, Alkaline phosphates, Alanine aminotransferase

Abstract

Liver disease and disorders are serious public health burdens because of the high prevalence among populations worldwide and poor long-term clinical outcome. The outcomes of the disease include deaths from liver decompensation, cirrhosis and HCC. Many liver diseases, including chronic HBV and HCV infection, ALD, NAFLD, autoimmune liver disease and drug-induced liver disease (DILI), potentially threaten a large proportion of the global population. The fuzzy soft set principle theory has been used for the developing of a diagnostic system in medicine and devise a prediction system named as fuzzy soft expert system which is a rule-based system uses fuzzy set and fuzzy soft set. There are five main components included in the basic structure, they are: (1) A fuzzication that translates the inputs (real-values) into fuzzy values, (2) obtaining fuzzy sets, (3) changing in to fuzzy soft sets, (4) reduction of normal parameter of fuzzy soft sets, (5) output data by algorithm. Fifty two individuals suspected and managed as HBV patients were involved in this study.All of them were attending liver diseases unit at Azadi teaching hospital in Duhok, Kurdistan Region-Iraq. They were being managed by the herpetology specialist as HBV infected patients. Their parameters (Alanine Aminotransferase (ALT), Aspartate aminotransferase (AST), Total Serum Albumin (Alb.), and Total Serum Bilirubin (T.S.Bil.)), were used as input data and the score of each patient was calculated. The developed fuzzy soft expert system was used to obtain the score for each as prognostic model for liver disorders. The score of 10 of those patients are selected and compared with the clinical status of each base on signs and symptoms of the HBV infection. Score more than 101.844 was considered to be highly linked with HBV infection. Scores less than 101.844 was considered to be not related to HBV infection.

Author Biographies

Ramadhan A. Mohammed, University of Duhok

Dept. of Mathematics, Faculty of Science, University of Duhok, Kurdistan-Region, Iraq

Ahmed M. Salih, University of Duhok

Medical research Center, College of Medicine, University of Duhok   

Muayad A. Mirza, University of Mosul

Department of Medicine, College of Medicine, University of Duhok

Tahir H. Ismail, University of Mosul

Dept. of Mathematics, College of com. Sci. and math., University of Mosul, Mosul-Iraq

Ahmed A. Allam, University of Assiut

Dept. of Mathematics, Faculty of Science, University of Assiut, Assiut-Egypt

References

Adam, F. and N. Hassan (2016). Multi Q-fuzzy soft expert set and its application. Journal of Intelligent & Fuzzy Systems30(2): 943-950.
Adlassnig, K. P. and G. Kolarz (1982). CADIAC-2: Computer-assisted medical diagnosis using fuzzy subsets. In M.M. Gupta, & E. Sanchez (Eds.), Approximate reasoning in decision analysis 219–247.
Bashir, M. and A. R. Salleh (2013). Possibility fuzzy soft expert set. Open Journal of Applied Sciences2(04): 208.
Booth, M. L., J. George, et al. (2008). The population prevalence of adverse concentrations and associations with adiposity of liver tests among Australian adolescents. Journal of paediatrics and child health44(12): 686-691.
Chen, D., E. Tsang, et al. (2005). The parameterization reduction of soft sets and its applications. Computers & Mathematics with Applications49(5): 757-763.
Chirinos, J. A., A. Veerani, et al. (2007). Evaluation of comorbidity scores to predict all-cause mortality in patients with established coronary artery disease. International journal of cardiology117(1): 97-102.
Cui, Y. and J. Jia (2013). Update on epidemiology of hepatitis B and C in China. Journal of gastroenterology and hepatology28(S1): 7-10.
Gamberger, D., N. Lavrač, et al. (2003). Active subgroup mining: a case study in coronary heart disease risk group detection. Artificial Intelligence in Medicine28(1): 27-57.
Genest, J. J., J. M. Bard, et al. (1991). Plasma apolipoprotein AI, A-II, B, E and C-III containing particles in men with premature coronary artery disease. Atherosclerosis90(2): 149-157.
Ghany, M. G., D. E. Kleiner, et al. (2003). Progression of fibrosis in chronic hepatitis C. Gastroenterology124(1): 97-104.
Hassan, N., O. R. Sayed, et al. (2016). Fuzzy Soft Expert System in Prediction of Coronary Artery Disease. International Journal of Fuzzy Systems: 1-14.
Hazaymeh, A. A., I. B. Abdullah, et al. (2012). Generalized fuzzy soft expert set. Journal of Applied Mathematics2012.
Hui, C.-K., T. Belaye, et al. (2003). A comparison in the progression of liver fibrosis in chronic hepatitis C between persistently normal and elevated transaminase. Journal of hepatology38(4): 511-517.
Kerner, A., O. Avizohar, et al. (2005). Association between elevated liver enzymes and C-reactive protein possible hepatic contribution to systemic inflammation in the metabolic syndrome. Arteriosclerosis, thrombosis, and vascular biology25(1): 193-197.
Kong, Z., L. Gao, et al. (2009). Comment on “A fuzzy soft set theoretic approach to decision making problems”. Journal of computational and applied mathematics223(2): 540-542.
Kong, Z., L. Gao, et al. (2008). The normal parameter reduction of soft sets and its algorithm. Computers & Mathematics with Applications56(12): 3029-3037.
Lazo, M., R. Hernaez, et al. (2011). Non-alcoholic fatty liver disease and mortality among US adults: prospective cohort study. Bmj343: d6891.
Lok, A. S. (2004). Prevention of hepatitis B virus–related hepatocellular carcinoma. Gastroenterology127(5): S303-S309.
Maji, P., A. R. Roy, et al. (2002). An application of soft sets in a decision making problem. Computers & Mathematics with Applications44(8): 1077-1083.
Maji, P. K., Biswas. R. and Roy. A. R (2001). Fuzzy soft set. Journal of Fuzzy Mathematics9: 589-602
Miller, R. A., H. E. Pople Jr, et al. (1982). Internist-I, an experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine307(8): 468-476.
Mohammed, R., T. Ismail, et al. (2016). Prognosis of Coronary Artery Disease Depending on Soft Sets. ANDAMIOS13(1).
Molodtsov, D. (1999). Soft set theory—first results. Computers & Mathematics with Applications37(4): 19-31.
Neshat, M., A. Masoumi, et al. (2014). Diagnosing hepatitis disease by using fuzzy hopfield neural network. Annual Research & Review in Biology4(17): 2709.
Neshat, M., M. Yaghobi, et al. (2008). Fuzzy expert system design for diagnosis of liver disorders. Knowledge Acquisition and Modeling, 2008. KAM'08. International Symposium on, IEEE.
Neshat, M. and A. E. Zadeh (2010). Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders. Intelligent Systems (IS), 2010 5th IEEE International Conference, IEEE.
Polat, K., S. Şahan, et al. (2007). Breast cancer and liver disorders classification using artificial immune recognition system (AIRS) with performance evaluation by fuzzy resource allocation mechanism. Expert Systems with Applications32(1): 172-183.
Ranjith, P., P. Baby, et al. (2003). ECG analysis using wavelet transform: application to myocardial ischemia detection. ITBM-RBM24(1): 44-47.
Rehm, J., A. V. Samokhvalov, et al. (2013). Global burden of alcoholic liver diseases. Journal of hepatology59(1): 160-168.
Roy, A. R. and P. Maji (2007). A fuzzy soft set theoretic approach to decision making problems. Journal of Computational and Applied Mathematics203(2): 412-418.
Vaisi-Raygani, A., H. Ghaneialvar, et al. (2010). The angiotensin converting enzyme D allele is an independent risk factor for early onset coronary artery disease. Clinical biochemistry43(15): 1189-1194.
Wiese, M., F. Berr, et al. (2000). Low frequency of cirrhosis in a hepatitis C (genotype 1b) single‐source outbreak in Germany: a 20‐year multicenter study. Hepatology32(1): 91-96.
Wiese, M., J. Fischer, et al. (2014). Evaluation of liver disease progression in the German hepatitis C virus (1b)‐contaminated anti‐D cohort at 35 years after infection. Hepatology59(1): 49-57.
Wung, S.-F. and B. Drew (1999). Comparison of 18-lead ECG and selected body surface potential mapping leads in determining maximally deviated ST lead and efficacy in detecting acute myocardial ischemia during coronary occlusion. Journal of electrocardiology32: 30-37.
Zadeh, L. A. (1965). Fuzzy sets. Information and control8(3): 338-353.

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Published

2017-03-30

How to Cite

Mohammed, R. A., Salih, A. M., Mirza, M. A., Ismail, T. H., & Allam, A. A. (2017). Prognosis of Liver Disorders in Dna Positive Hbv Patients Based on Fuzzy Soft Sets. Science Journal of University of Zakho, 5(1), 112–119. https://doi.org/10.25271/2017.5.1.311

Issue

Vol. 5 No. 1 (2017): March, 2017

Section

Science Journal of University of Zakho

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