信阳师范大学学报（自然科学版）2026，Vol.39Issue(1)：58-65,8.DOI:10.3969/j.issn.2097-583X.2026.01.008
基于特征选择算法的DBN-SVM胃癌生存期分类方法
A feature selection algorithm based on DBN-SVM classification for gastric cancer survival
摘要
Abstract
In order to reduce the dimensionality of the dataset to obtain the best feature subset as well as to improve the accuracy of the prognostic survival classification of gastric cancer,a hybrid network model of deep belief network and support vector machine combined with feature selection algorithm was proposed.Based on the filtered feature selection algorithm,a distance coefficient was introduced to adjust the overall degree of bias and reduce the instability of the calculated weight values,so as to construct new sample weight values,and then analyze the subset of features that have a greater impact on the survival period of gastric cancer through the Pearson's correlation coefficient;The constrained Boltzmann machine module was adopted in the deep belief network,and then the subset of features in the hidden layer was subjected to the feature extraction;Finally,the support vector machine was used to classify the output values of the last layer of the deep belief network to realize the classification of gastric cancer survival.By improving the feature selection algorithm and combining the advantages of deep belief network and support vector machine,the model showed better accuracy,AUC value and F1 value in the experiments,which are 81.2%,83.4%and 81.5%,respectively,compared with the traditional single machine learning method.关键词
深度置信网络/支持向量机/过滤式特征选择算法/特征提取/胃癌生存期Key words
deep belief networks/support vector machines/filtered feature selection algorithm/feature extraction/gastric cancer survival分类
信息技术与安全科学引用本文复制引用
刘道华,余长鸣,周秋菊,王秋岱..基于特征选择算法的DBN-SVM胃癌生存期分类方法[J].信阳师范大学学报（自然科学版）,2026,39(1):58-65,8.基金项目
国家自然科学基金项目(31872704) （31872704）
河南省科技攻关项目(222102210265) （222102210265）
河南省本科高校研究性教学改革项目(2022SYJXLX061) （2022SYJXLX061）
河南省高等学校重点科研项目(22A520007) （22A520007）
河南省研究生教育改革与质量提升工程项目(YJS2024AL104) （YJS2024AL104）
