信阳师范大学学报（自然科学版）2025，Vol.38Issue(3)：297-303,7.DOI:10.3969/j.issn.2097-583X.2025.03.007
基于贝叶斯优化的支持向量回归预测帕金森病严重程度研究
Predicting Parkinson's disease severity using support vector regression based on Bayesian optimization
摘要
Abstract
Based on multimodal data from public datasets,including demographic characteristics,clinical features and imaging features,a support vector regression model optimized through Bayesian optimization was proposed to accurately predict the severity of Parkinson's disease.Experimental results demonstrated that the model not only exhibited high accuracy in predicting Parkinson's disease severity but also showed significant explanatory power.Through feature importance analysis,the key features that contribute most significantly to the predictive model were effectively identified,which not only provides a solid scientific basis for clinical management and treatment decisions in Parkinson's disease,but also opens up new research perspectives for in-depth exploration of the pathological mechanisms of this disease.关键词
帕金森病/支持向量回归/贝叶斯优化/多模态数据分析/功能核磁共振成像Key words
Parkinson's disease/support vector regression/Bayesian optimization/multimodal data analysis/functional magnetic resonance imaging分类
医药卫生引用本文复制引用
王敬,王朋威,谢晓,韩红芳..基于贝叶斯优化的支持向量回归预测帕金森病严重程度研究[J].信阳师范大学学报（自然科学版）,2025,38(3):297-303,7.基金项目
国家自然科学基金项目(31900710) （31900710）
信阳师范大学南湖学者奖励计划青年项目 （）
