计算机应用与软件2024，Vol.41Issue(6)：250-256,7.DOI:10.3969/j.issn.1000-386x.2024.06.037
基于特征进化选择随机森林的MCI自动诊断
MCI AUTOMATIC DIAGNOSIS BASED ON FEATURE EVOLUTION AND RANDOM FOREST SELECTION
摘要
Abstract
In recent years,research on the condition of mind cognitive impairment(MCI),which is the normal and excessive stage of Alzheimer's disease,has attracted much attention.However,the current medical MCI manual diagnosis not only has relatively large limitations in the referenced features,but also relies on manual judgment,which is prone to subjective errors.Therefore,this paper proposes an automatic diagnosis method of MCI based on random forest,hoping to determine MCI efficiently and accurately through machine learning.At the same time,in order to obtain the optimal parameters of the random forest MCI diagnosis model more efficiently,genetic algorithm was combined.The results show that the accuracy of this method is about 5％higher than that of medical manual diagnosis,and the time taken by genetic algorithm is shortened by nearly 45 times compared with grid search on the problem of obtaining the optimal parameters of random forest.关键词
MCI/随机森林/遗传算法/最优参数Key words
MCI/Random forest/Genetic algorithm/Optimal parameters分类
信息技术与安全科学引用本文复制引用
高峰,郑丽丽,顾进广..基于特征进化选择随机森林的MCI自动诊断[J].计算机应用与软件,2024,41(6):250-256,7.基金项目
国家自然科学基金项目(U1836118) （U1836118）
富媒体数字出版内容组织与知识服务重点实验室开放基金项目(ZD2020/09-01) （ZD2020/09-01）
教育部新一代信息技术创新项目(2018A03025). （2018A03025）
