[1]李彩,范炤.基于机器学习的阿尔兹海默症分类预测[J].中国医学物理学杂志,2020,37(3):379-384.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.023]
 LI Cai,FAN Zhao.Classification and prediction of Alzheimer’s disease based on machine learning[J].Chinese Journal of Medical Physics,2020,37(3):379-384.[doi:DOI:10.3969/j.issn.1005-202X.2020.03.023]
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基于机器学习的阿尔兹海默症分类预测()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第3期
页码:
379-384
栏目:
其他(数字医学等)
出版日期:
2020-03-25

文章信息/Info

Title:
Classification and prediction of Alzheimer’s disease based on machine learning
文章编号:
1005-202X(2020)03-0379-06
作者:
李彩1范炤2
1.山西医科大学基础医学院, 山西 太原 030001; 2.山西医科大学转化医学研究中心, 山西 太原 030001
Author(s):
LI Cai1 FAN Zhao2
1. School of Basic Medicine, Shanxi Medical University, Taiyuan 030001, China; 2. Translational Medicine Research Center of Shanxi Medical University, Taiyuan 030001, China
关键词:
阿尔兹海默症机器学习L1正则Logistic回归分类预测
Keywords:
Keywords: Alzheimer’s disease machine learning L1-regularized Logistic regression classification and prediction
分类号:
R318;R741
DOI:
DOI:10.3969/j.issn.1005-202X.2020.03.023
文献标志码:
A
摘要:
目的:应用机器学习方法,将脑结构磁共振(sMRI)、年龄、性别、受教育年限和MMSE量表评分作为特征,对阿尔兹海默症进行分类预测。方法:特征选择后,用L1正则Logistic回归、L1正则支持向量机、梯度提升树分别对脑sMRI数据进行分类预测,选出最优模型后引入年龄、性别、受教育年限和MMSE量表评分特征优化模型,用10-折交叉验证评价模型性能。结果:L1正则Logistic回归分类效果最好,加入年龄、性别、受教育年限和MMSE评分后预测准确率提高0.89%~11.42%。结论:L1正则化Logistic回归模型的sMRI+年龄+性别+受教育年限+MMSE评分特征集对阿尔兹海默症有更好的分类效果,可作为辅助诊断阿尔兹海默症的依据。
Abstract:
Abstract: Objective To classify and predict Alzheimer’s disease by machine learning method with brain structural magnetic resonance imaging (sMRI), age, gender, years of education and MMSE score as features. Methods After feature selection, L1-regularized Logistic regression, L1-regularized support vector machine and gradient boosting decision tree were used to classify and predict the brain sMRI data. After the optimal model was determined, the model was optimized by several features including age, gender, years of education and MMSE scores, and the performance of the optimized model was evaluated by 10-fold cross- validation. Results L1-regularized Logistic regression had the best classification performance. The prediction accuracy of the model optimized by the features of age, gender, years of education and MMSE scores was increased by 0.89% to 11.42%. Conclusion The feature set of sMRI, age, gender, years of education and MMSE scores in L1-regularized Logistic regression model has a better performance on the classification of Alzheimer’s disease, which can be used as the basis for the auxiliary diagnosis of Alzheimer’s disease.

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备注/Memo

备注/Memo:
【收稿日期】2019-11-20 【基金项目】留学回国人员科技活动项目(619017);山西省回国留学人员科研资助项目(2016-061) 【作者简介】李彩,硕士研究生,研究方向:阿尔兹海默症的分类预测,E-mail: licaizn@163.com 【通信作者】范炤,博士,副教授,研究方向:机器学习在阿尔兹海默症临床诊断的应用,E-mail: Fanzhao316@163.com
更新日期/Last Update: 2020-04-02