Fine Tuning Data Mining Algorithm for an Efficient Classification of E-Coli
A.Rama1, A.Gayathri2, S.Christy3
1A.Rama, Assistant Professor (Sg), Department Of Computer Science And Engineering, Saveetha School Of Engineering, Simats, Chennai, India
2A.Gayathri, Associate Professor, Department Of Computer Science And Engineering, Saveetha School Of Engineering, Simats, Chennai, India
3S.Christy, Assistant Professor (Sg), Department Of Information Technology,Saveetha School Of Engineering, Simats, Chennai, India.
Manuscript received on October 14, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 109-113 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3936119119/2019©BEIESP | DOI: 10.35940/ijitee.3936.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: E. coli is the first organisms to be sequenced as genome and the classification within the DEC pathotypes has epidemiologic and clinical implications for managing diarrheal diseases. In many developing countries E.coli leads to cause of diarrhea in children. where the mode of transmissions takes place via food and water. based on their pathogenic phenotype and diseases they cause it can be classified into 6 groups. consequently, our awareness of the spectrum of diseases and syndromes that they cause is quite limited. Also, because we cannot readily identify infected patients, there are many complexities in defining the modes of attainment, prevention and treatment strategies, and estimating the burden of infectious squealed. These infections create many challenges, and no progress will take place until the diagnostic potential for these agents got improved. Identifying E. coli isolate co-express LA reiterates the difficulty of assigning bacteria to groups on the basis of their adherence phenotype or genotype. Therefore the analysis of E -coli with molecular methods demonstrates that strains carry will represent more characteristics of typical EPEC and also the lack of AggR regulon, we propose a novel classification approach for classifying E-coli therefore to recognize pathogens. In addition, the ability to simultaneously induce attaching effacing lesions and biofilm production may enhance the potential of the strains to cause diarrhea and prolong bacterial residence in the intestines, thus worsening malnutrition in the patients.
Keywords: E coli , Classification , Bayes Net, Navie Bayes, RBF Network, SMO
Scope of the Article: Classification