Application of Improved Deep Extreme Learning Machine in the Classification of Traditional Chinese Medicine Syndromes of Lung Cancer
- VernacularTitle:改进深度极限学习机在肺癌中医证型分类中的应用研究
- Author:
Xinyou ZHANG
1
;
Huakang XU
;
Xiaoling ZHOU
;
Mengling LIU
;
Xiuyun LI
;
Yaming ZHANG
;
Chunqiang ZHANG
;
Liping TANG
Author Information
- Keywords: Lung cancer; Syndrome classification; Deep extreme learning machine; Feature selection
- From: World Science and Technology-Modernization of Traditional Chinese Medicine 2023;25(6):2132-2139
- CountryChina
- Language:Chinese
- Abstract: Objective To use feature selection and Likert grading method to quantify the data of lung cancer medical records,to construct a deep extreme learning machine model optimized by the sparrow search algorithm,to classify and predict the syndrome types of traditional Chinese medicine medical record data of lung cancer,and to provide scientific and effective research on syndrome type classification of traditional Chinese medicine.means.Methods The medical records of 497 cases diagnosed with lung cancer from January 2015 to December 2021 were collected from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine,and 412 medical records were screened as the research objects.Syndromic factors of different syndromes were summarized by feature selection and feature importance ranking,and the syndrome factors were quantified by Likert grading method.Build a deep extreme learning machine optimized based on the sparrow search algorithm,and train and test the model.Finally,the model built in this paper is compared with other machine learning models according to three evaluation criteria.Results The average classification accuracy of the SSA-DELM model established in this paper was 88.44%,while the average accuracy of the support vector machine and Bayesian network was 83.39%and 84.53%,respectively.The recall rate and F1 value of the SSA-DELM model on the five syndrome types are mostly above 80%,which is also better than other traditional machine learning models.Conclusion The results of the study show that the use of feature selection combined with Likert grading method to quantify the lung cancer medical record data,compared with the 0-1 processing data,can show the characteristics of the data,improve the accuracy of the classification model,SSA-DELM new Compared with other traditional machine learning classification models,the model has better representation learning ability and learning speed.This model not only provides a scientific and technical means for the clinical treatment of lung cancer,but also provides a useful reference for the informatization and intelligent development of TCM syndrome differentiation and treatment.
