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
高峰 1郑丽丽 1顾进广1
作者信息
- 1. 武汉科技大学计算机科学与技术学院 湖北武汉 430065;武汉科技大学大数据科学与工程研究院 湖北武汉 430065;湖北省智能信息处理与实时工业系统重点实验室 湖北武汉 430065;富媒体数字出版内容组织与知识服务重点实验室 湖北武汉 430065
- 折叠
摘要
近年来,作为正常与阿尔茨海默病过渡阶段的轻度认知障碍(Mild Cognitive Impairment,MCI)病症的研究备受关注.但目前的医学MCI人工诊断不仅参考的特征局限性较大,且依靠人工判定,易产生主观上的误差.因此,提出一种基于随机森林的MCI自动诊断方法,通过机器学习的方式,高效准确地判定MCI;同时应用遗传算法更高效地搜索求解模型的最优参数.结果表明,该方法与医学人工诊断方式相比准确率提高约5％,且在求取随机森林的最优参数问题上,与网格搜索相比,遗传算法所用时间约为其1/45.
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引用本文复制引用
基金项目
国家自然科学基金项目(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)
教育部新一代信息技术创新项目(2018A03025)
出版年
2024
国家科技期刊平台
NSTL
万方数据