Tree-Based Prediction of Influential Factors and Information Mining
DOI: 10.23977/acss.2023.071108 | Downloads: 11 | Views: 536
Author(s)
Xin Tan 1
Affiliation(s)
1 Hubei University of Education of Mathematics and Statistics, Wuhan, Hubei, 430205, China
Corresponding Author
Xin TanABSTRACT
In minimally invasive gastrointestinal surgery (IPI), local sedative and analgesic drugs are required, and a new type of drug, "R-drug", has yet to be studied non-intervention ally. This paper analyzes and explores the vital signs, adverse effects and patient satisfaction of IPI based on the real performance data of new and traditional sedative drugs in clinical trials. In this paper, we first cleaned, coded and normalized the data, then based on multivariate visualization analysis, we found that there were significant differences between different drug groups regarding each adverse reaction, and we conducted chi-square test on different drug groups regarding each adverse reaction, and we found that there were significant differences between different drug groups regarding intra-operative adverse reactions, and only "nausea and vomiting" and "abdomen and vomiting" were found in the post-operative adverse reactions. Among the postoperative adverse reactions, only "nausea and vomiting" and "abdominal distension and abdominal pain" showed significant differences. Regarding the prediction of adverse reactions, this paper up-sampled the dataset and built a model based on the K nearest neighbor algorithm, and the classification AUC of the model on the tested dataset was above 0.92, and the confusion matrix and ROC diagram were made to visualize the specific testing of the model.
KEYWORDS
Traffic Flow; Large-scale Gathering Activities; Traffic Congestion; Macro Base Map; VISSIM SimulationCITE THIS PAPER
Xin Tan, Tree-Based Prediction of Influential Factors and Information Mining. Advances in Computer, Signals and Systems (2023) Vol. 7: 49-54. DOI: http://dx.doi.org/10.23977/acss.2023.071108.
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