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Mining the Contact Lens Adhering Bacteria through Machine Learning and Clinical Analysis
Objectives: Even when studies report most of the Contact Lens (CLs) wearers possess improved vision, there are some potential risks with the development of microbial keratitis. This is in turn creates research issue under public health concern. Methods/Analysis: The methodology of the work determines the culture sensitivity of the recovered isolates from three different CLs users: Daily disposable lens, monthly disposable lens and yearly disposable lens. Findings: Through the machine learning tool called Waikato Environment for Knowledge Analysis (WEKA) and extensive clinical laboratory analysis, the study provides information on prevalent Contact Lens adhering bacteria involved in causing keratitis and examine microbial biofilm formation using Scanning Electron Microscopic (SEM) analysis. The sample type of the lens with the bacterial infections were then statistically analyzed, so that the knowledge mined would aid the medical practitioners in the treatment of bacterial keratitis. Novelty/Improvement: The present study supports the treatment of bacterial keratitis associated with Contact Lens users to reduce or to prevent the adverse effects caused by bacterial pathogens.
Bacteria, Clinical, Contact Lens, Keratitis, Knowledge.
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