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Gel is a post-operative cleaning material with antibacterial effect, which helps patients recover after surgery. It is more and more popular in surgery, but it is still controversial in use. This study collected the electronic medical records of patients in a hospital for nearly three years, using a combination of a variety of special selection methods to process data and using random forest, support vector machine, LightGBM and XGBoost and other machine learning methods to predict the suitability of patients. The results show that polysaccharide gel is not suitable for all people, whether to use it should consider different situations. This paper has studied the applicability of medical gels to patients, and established a predictability model to provide data support for the clinical application of this expensive medical material.
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