Potential of Hybrid Adaptive Neuro Fuzzy Model in Simulating Clostridium Difficile Infection Status
Ahmed Nouri Alsharksi¹, Y.A Danmaraya², Hadiza Usman Abdullahi³, Umar Muhammad Ghali⁴, A.G Usman^5
1A.G Usman*, Faculty of Pharmacy, Department of Analytical Chemistry, Near East University, Nicosia, Turkish Republic of Northern Cyprus. 
²Ahmed Nouri Alsharksi, Department of Biochemistry, faculty of Medicine, Misurata University, Libya.
³Y.A Danmaraya, Department of Chemistry, Faculty of Science, Yusuf Maitama Sule University, Ado Bayero house, Kano, Nigeria.
⁴Hadiza Usman Abdullahi, Department of Chemistry, Faculty of Science, Federal University Dutse, Jigawa, Nigeria.
⁵Umar Muhammad Ghali, Faculty of Medicine, Department of Medical biochemistry, Near East University, Nicosia, Turkish Republic of Northern Cyprus.
Manuscript received on June 30, 2020. | Revised Manuscript Received on July 20, 2020. | Manuscript published on July 20, 2020. | PP: 1-6 | Volume-3 Issue-1, July 2020. | Retrieval Number: A0191063120/2020©LSP | DOI: 10.35940/ijbsac.A0191.073120
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The global burden posed by nosocomial diarrhea lead to the strong given attention by health practitioners science its morbidity and mortality rate hit about 500,000 rates annually in the United states. Diagnostic measures have been put in place to detect the presence of CD using different methods. Reliable prediction of the health status of patients is of paramount importance. This study aimed at investigating the status of stool samples collected to test the presence of clostridium difficile as either positive or negative from both inpatient and outpatient from the record units of Near East University Hospital using hybrid adaptive neuro fuzzy (known as ANFIS) model consisting of various combinations of membership functions and training Fis. In this context, the age of the patients, gender, results of the analysis conducted, the department in which the patient was admitted, the age category and the hospitalization were employed as the input variables. The performance accuracy of these membership functions and training FIS combinations were checked using two performance indices determination coefficient (R2) and mean square error (MSE). The obtained computation data driven models proves the reliability of the combination of subtractive clustering membership function and hybrid training FIS over the other three ANFIS combinations. Overall, the results indicated the reliability and satisfaction of hybrid adaptive neuro fuzzy in checking the status of stool samples collected to test the presence of clostridium difficile as either positive or negative from both inpatient and outpatient.
Keywords: Clostridium difficile; Stool samples; Hybrid adaptive neuro fuzzy; Membership functions; Training FIS.