Endometrial tumor classification marking method based on random forest

Abstract

The invention provides an endometrial tumor classification marking method based on a random forest, and the method comprises the steps of obtaining endometrial malignant and benign tumor data as sample data, carrying out the normalization processing, and dividing a test set and a plurality of training sets; performing decision tree training on each training set to obtain a corresponding CART decision tree model; evaluating all features of each CART decision tree model to obtain a corresponding feature set; selecting an optimal feature from each feature set through Gini index comparison to perform branching processing to obtain a decision tree and form a random forest model; optimizing the random forest model by adopting a particle swarm algorithm and importing the random forest model intoa test set to obtain a trained random forest model; acquiring endometrial tumor data to be detected, importing the endometrial tumor data into the trained random forest model, and distinguishing benign or malignant tumor data. By implementing the invention, continuous, discrete and mixed endometrial tumor data sets can be processed, and the problem that the accuracy is quickly reduced under the condition that more data is lacked can be solved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for classifying and marking endometrial tumors based on random forests. Background technique [0002] In the rapidly developing Internet era, the emergence and application of machine learning is profoundly changing the medical industry. Prior to this, the collection and analysis of medical data was a challenging and difficult task. But now, through the analysis and processing of data, machine learning can accurately and clearly realize the set plan and deliver the results. [0003] At present, there are relatively few related studies on endometrial tumor data at home and abroad. Most of the classification models used in data prediction are KNN algorithm, neural network algorithm, Bayesian algorithm, etc., but the accuracy of these algorithms is not very ideal, and they cannot handle continuous, discrete, and mixed large data sets, especially when missing data i...

AI Technical Summary

Problems solved by technology

Most of the classification models used in data prediction are KNN algorithm, neural network algorithm, Bayesian algorithm, etc., but the accuracy of these algorithms is not very ideal, and they cannot handle continuous, discrete, and mixed large data sets, especially when missing data is large. In the case of many, the accuracy will decrease rapidly with missing data

[0004] Therefore, there is an urgent need for an effective algorithm to analyze endometrial tumor data, which can handle continuous, discrete and mixed large data sets, and can overcome the problem of rapid decrease in accuracy when there is a lot of data shortage

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