Smart medical uses the medical information platform and the current technological means to enable the process of sharing information between medical staff and medical equipment. The combination of current technology and the medical field has become the norm. In the future, more artificial intelligence technologies will be integrated into the medical field to promote the development of medical care. At present, the information on the Internet is very large and complex, and general search engines often do not have knowledge in certain professional fields and can only perform shallow keyword searches. Therefore, it is difficult to meet people’s medical diagnosis needs, and smart medical care can solve these needs. Medical imaging refers to the technology or process of obtaining internal tissue images of a certain part of the human body for medical research, including medical imaging systems and medical image processing. Medical image processing refers to the further processing of the obtained images, the purpose of which is either to restore the original image that was not clear enough or to highlight some characteristic information in the image. The purpose of this paper is to study the research on the selection of T1 stage renal tumor resection based on the imaging MAP score under smart medical care. It is hoped that through smart medicine and medical imaging technology, it can help renal tumor resection, reduce the sequelae of renal tumor resection, and promote the development of medical services. This paper proposes applying natural language processing technology to the medical field, creating an intelligent diagnosis assistance system, and using the existing medical record data to realize the corresponding medical assistance functions. It studies the class imbalance problem prevalent in medical datasets and provides better solutions through ensemble learning techniques to improve classifier performance when the number of positive and negative samples is unbalanced. The experimental results in this paper show that the creatinine of patients undergoing renal tumor resection combined with smart medicine and imaging technology is stable at 75 mol/L, while the creatinine is stable at 71 mol/L in other methods. It shows that the postoperative effect of smart medical treatment and imaging technology is better.
How to provide renters with rent reference based on housing characteristics is an urgent issue to be solved, and GBRT (Gradient Boosting Regression Tree) provides a solution to the rent prediction problem. However, in GBRT, there is a problem that the accuracy of model prediction is not ideal due to the fact that the average value is used as the output value for the subset of node samples when constructing a tree, and all training samples arriving at a certain leaf node are equally considered, which relies too heavily on data quality. This paper uses KNN algorithm to perform weighted averaging based on the contribution of neighboring points to the prediction results, and combines the advantages of SVR in processing high-dimensional data and small samples, and proposes SVR and KNN_GBRT fusion model. The improved fusion model has been validated in a housing rental datasets and has better prediction results compared to SVR model and GBRT model.
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