空军军医大学学报2025，Vol.46Issue(5)：630-632,638,4.DOI:10.13276/j.issn.2097-1656.2025.05.011
基于支持向量机算法的部队官兵心理健康分类研究
Research on the classification of mental health of officers and soldiers based on support vector machine algorithm
摘要
Abstract
Objective To train the sample features by using support vector machine(SVM)algorithm,and to use machine learning for mental health status,so as to realize automatic classification and recognition.Methods Firstly,undersampling method was used to solve the problem of sample imbalance,then normalization method was used to control the scores of all indicators in an interval,and samples were trained by selecting different kernel functions of SVM.Finally,hyperparameters were tuned by grid search to obtain the best parameter combination,and the samples were tested again to obtain the evaluation report of the model.Results The results showed that the accuracy,recall and F1-Score of the algorithm based on Sigmoid and RBF kernel functions were improved after grid search parameter tuning.Conclusion This paper provides an idea for the intelligent assessment of psychological risk,which can be applied to other scenarios with a little modification.关键词
支持向量机/归一化/网格搜索/心理测试Key words
support vector machine/normalization/grid search/psychological test分类
计算机与自动化引用本文复制引用
张利利,惠铎铎,党维涛..基于支持向量机算法的部队官兵心理健康分类研究[J].空军军医大学学报,2025,46(5):630-632,638,4.基金项目
陕西省重点研发项目(2022SF-137,2023-YBSF-509) （2022SF-137,2023-YBSF-509）
