| 蒲秀娟,任青,韩亮,谈云帆,刘媛.使用双优先预测层次模型联合决策的阿尔茨海默症预测方法[J].电子测量与仪器学报,2025,39(4):16-25 |
| 使用双优先预测层次模型联合决策的阿尔茨海默症预测方法 |
| Method on Alzheimer’s disease prediction based on joint decision-makingutilizing dual priority prediction hierarchical model |
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| DOI： |
| 中文关键词: 阿尔茨海默症预测 层次模型 磁共振成像 认知评分 统计特征 |
| 英文关键词:Alzheimer’s disease prediction hierarchical model magnetic resonance imaging cognitive scores statistical feature |
| 基金项目:国家自然科学基金（62171066）项目资助 |
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| Author | Institution |
| Pu Xiujuan | 1.School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China;
2.Chongqing Key Lab of Bio-perception & Multimodal Intelligent Information Processing, Chongqing 401331, China |
| Ren Qing | School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China |
| Han Liang | 1.School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China;
2.Chongqing Key Lab of Bio-perception & Multimodal Intelligent Information Processing, Chongqing 401331, China |
| Tan Yunfan | School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China |
| Liu Yuan | School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China |
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| 中文摘要: |
| 在阿尔茨海默症(AD)患者出现晚期症状之前，能够准确预测AD进展对于及时采取适当的治疗和干预措施至关重要。提出一种使用双优先预测层次模型联合决策的AD预测方法，将AD、轻度认知障碍(MCI)和认知正常(NC)3类别预测问题转化为两个层次的两类别预测问题。首先，从个体历史随访所获取的磁共振成像(MRI)和认知评分(CSs)两种模态的时间序列数据中提取统计特征，并使用累计加权嵌入式特征选择方法从MRI体积统计特征中选择出高重要性MRI体积统计特征；然后，构建NC优先预测层次模型和AD优先预测层次模型，利用提取得到的高重要性MRI体积统计特征和CSs统计特征，使用这两个层次模型的不同层次的预测结果进行联合决策，优先预测出样本中的NC个体和AD个体；最后确定MCI个体，实现AD/MCI/NC 3类别预测。在TADPOLE数据集上进行实验，AD预测方法的准确率为89.29%，F1分数的宏平均值为88.81%。实验结果表明，AD预测方法是有效的，且其性能优于传统的AD预测方法。 |
| 英文摘要: |
| Accurately predicting Alzheimer’s disease(AD) progression is crucial for timely treatment and intervention before advanced stage of AD. In this paper, a method on AD prediction based on joint decision-making utilizing dual priority prediction hierarchical model is proposed, which converts the three category prediction problem on AD, mild cognitive impairment(MCI) and normal cognitive(NC) into two levels of two category prediction problem. Firstly, the statistical features are extracted from the time series data of magnetic resonance imaging(MRI) and cognitive scores(CSs), which is obtained from individual historical follow up, and the high-importance MRI volume statistical features are selected using the cumulative weighted embedded feature selection method. Then, both the NC priority prediction hierarchical model and the AD priority prediction hierarchical model are constructed. Using the selected high-importance MRI volume statistical features and CSs statistical features, these two hierarchical models are used to achieve AD/MCI/NC three category prediction. The NC and AD individuals are first predicted, and finally the MCI individuals are determined. The proposed AD prediction method is evaluated on the TADPOLE dataset. The accuracy(ACC) and macro average of F1 score of the proposed AD prediction method are 89.29% and 88.81%, respectively. The experimental results show that the proposed method is effective and better than conventional AD prediction method. |
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