A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method
Based on proposed joint human connectome project multi-modal parcellation (JHCPMMP), the study on the binary classification of Alzheimer’s disease was conducted. We tried to build a novel classification model, which can be interpretative and have the ability to deal with the complexity an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9809963/ |
| _version_ | 1818532058451935232 |
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| author | Jinhua Sheng Shuai Wu Qiao Zhang Zhongjin Li He Huang |
| author_facet | Jinhua Sheng Shuai Wu Qiao Zhang Zhongjin Li He Huang |
| author_sort | Jinhua Sheng |
| collection | DOAJ |
| description | Based on proposed joint human connectome project multi-modal parcellation (JHCPMMP), the study on the binary classification of Alzheimer’s disease was conducted. We tried to build a novel classification model, which can be interpretative and have the ability to deal with the complexity and individual differences of brain networks. The subclass weighted logistic regression (SWLR) based on logistic regression was proposed in this paper. We conducted five groups of experiments, in which the accuracy of HC vs. AD was 95.8%, HC vs. EMCI was 91.6%, HC vs. LMCI was 93.7%, EMCI vs. LMCI was 89.5%, and LMCI vs. AD was 91.6%. In addition, we conducted a follow-up analysis of the coefficient matrix and found that the distribution of core deterioration brain regions in different stages is different in the development of Alzheimer’s disease. We located these brain regions in two-dimensional images and found that they generally show a trend of continuous counterclockwise migration. |
| first_indexed | 2024-12-11T17:40:34Z |
| format | Article |
| id | doaj.art-d5316df8a17e4f27897464f630b550a2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T17:40:34Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d5316df8a17e4f27897464f630b550a22022-12-22T00:56:33ZengIEEEIEEE Access2169-35362022-01-0110688466885610.1109/ACCESS.2022.31868889809963A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression MethodJinhua Sheng0https://orcid.org/0000-0002-7662-9126Shuai Wu1https://orcid.org/0000-0002-2091-5684Qiao Zhang2Zhongjin Li3He Huang4College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaCollege of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaBeijing Hospital, Beijing, ChinaCollege of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaCollege of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, ChinaBased on proposed joint human connectome project multi-modal parcellation (JHCPMMP), the study on the binary classification of Alzheimer’s disease was conducted. We tried to build a novel classification model, which can be interpretative and have the ability to deal with the complexity and individual differences of brain networks. The subclass weighted logistic regression (SWLR) based on logistic regression was proposed in this paper. We conducted five groups of experiments, in which the accuracy of HC vs. AD was 95.8%, HC vs. EMCI was 91.6%, HC vs. LMCI was 93.7%, EMCI vs. LMCI was 89.5%, and LMCI vs. AD was 91.6%. In addition, we conducted a follow-up analysis of the coefficient matrix and found that the distribution of core deterioration brain regions in different stages is different in the development of Alzheimer’s disease. We located these brain regions in two-dimensional images and found that they generally show a trend of continuous counterclockwise migration.https://ieeexplore.ieee.org/document/9809963/Alzheimer’s diseasehuman connectome project (HCP)multi-modal parcellation (MMP)mild cognitive impairment (MCI)subclass-weighting |
| spellingShingle | Jinhua Sheng Shuai Wu Qiao Zhang Zhongjin Li He Huang A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method IEEE Access Alzheimer’s disease human connectome project (HCP) multi-modal parcellation (MMP) mild cognitive impairment (MCI) subclass-weighting |
| title | A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method |
| title_full | A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method |
| title_fullStr | A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method |
| title_full_unstemmed | A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method |
| title_short | A Binary Classification Study of Alzheimer’s Disease Based on a Novel Subclass Weighted Logistic Regression Method |
| title_sort | binary classification study of alzheimer x2019 s disease based on a novel subclass weighted logistic regression method |
| topic | Alzheimer’s disease human connectome project (HCP) multi-modal parcellation (MMP) mild cognitive impairment (MCI) subclass-weighting |
| url | https://ieeexplore.ieee.org/document/9809963/ |
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