Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model
BackgroundAssessing cognitive function in patients with chronic obstructive pulmonary disease (COPD) is crucial for ensuring treatment efficacy and avoiding moderate cognitive impairment (MCI) or dementia. We aimed to build better machine learning models and provide useful tools to provide better gu...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1473223/full |
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| author | Wenwen Cheng Chen Yu Xiaohui Liu |
| author_facet | Wenwen Cheng Chen Yu Xiaohui Liu |
| author_sort | Wenwen Cheng |
| collection | DOAJ |
| description | BackgroundAssessing cognitive function in patients with chronic obstructive pulmonary disease (COPD) is crucial for ensuring treatment efficacy and avoiding moderate cognitive impairment (MCI) or dementia. We aimed to build better machine learning models and provide useful tools to provide better guidance and assistance for COPD patients' treatment and care.MethodsA total of 863 COPD patients from a local general hospital were collected and screened, and they were separated into two groups: cognitive impairment (356 patients) and cognitively normal (507 patients). The Montreal Cognitive Assessment (MoCA) was used to test cognitive function. The swarm intelligence optimization algorithm (SIOA) was used to direct feature weighting and hyperparameter optimization, which were considered simultaneous activities. A self-assigning feature weights and residual evolution (SAFWRE) algorithm was built on the concept of linear and nonlinear information fusion.ResultsThe best method in SIOA was the circle search algorithm. On the training set, SAFWRE's ROC-AUC was 0.9727, and its PR-AUC was 0.9663; on the test set, SAFWRE's receiver operating characteristic-area under curve (ROC-AUC) was 0.9243, and its precision recall-area under curve (PR-AUC) was 0.9059, and its performance was much superior than that of the control technique. In terms of external data, the classification and prediction performance of various models are comprehensively evaluated. SAFWRE has the most excellent classification performance, with ROC-AUC of 0.8865 and pr-auc of 0.8299.ConclusionThis work develops a practical visualization system based on these weight attributes which has strong application importance and promotion value. |
| format | Article |
| id | doaj-art-83ef4d17071a48c199c0e92c513c343c |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-83ef4d17071a48c199c0e92c513c343c2025-02-07T06:49:26ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-02-01810.3389/frai.2025.14732231473223Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution modelWenwen Cheng0Chen Yu1Xiaohui Liu2Military Preventive Medicine School, Air Force Medical University, Xi'an, ChinaMilitary Preventive Medicine School, Air Force Medical University, Xi'an, ChinaThe 986th Hospital of PLAAF, Air Force Medical University, Xi'an, ChinaBackgroundAssessing cognitive function in patients with chronic obstructive pulmonary disease (COPD) is crucial for ensuring treatment efficacy and avoiding moderate cognitive impairment (MCI) or dementia. We aimed to build better machine learning models and provide useful tools to provide better guidance and assistance for COPD patients' treatment and care.MethodsA total of 863 COPD patients from a local general hospital were collected and screened, and they were separated into two groups: cognitive impairment (356 patients) and cognitively normal (507 patients). The Montreal Cognitive Assessment (MoCA) was used to test cognitive function. The swarm intelligence optimization algorithm (SIOA) was used to direct feature weighting and hyperparameter optimization, which were considered simultaneous activities. A self-assigning feature weights and residual evolution (SAFWRE) algorithm was built on the concept of linear and nonlinear information fusion.ResultsThe best method in SIOA was the circle search algorithm. On the training set, SAFWRE's ROC-AUC was 0.9727, and its PR-AUC was 0.9663; on the test set, SAFWRE's receiver operating characteristic-area under curve (ROC-AUC) was 0.9243, and its precision recall-area under curve (PR-AUC) was 0.9059, and its performance was much superior than that of the control technique. In terms of external data, the classification and prediction performance of various models are comprehensively evaluated. SAFWRE has the most excellent classification performance, with ROC-AUC of 0.8865 and pr-auc of 0.8299.ConclusionThis work develops a practical visualization system based on these weight attributes which has strong application importance and promotion value.https://www.frontiersin.org/articles/10.3389/frai.2025.1473223/fullchronic obstructive pulmonary diseasemild cognitive impairmentMontrealAIML |
| spellingShingle | Wenwen Cheng Chen Yu Xiaohui Liu Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model Frontiers in Artificial Intelligence chronic obstructive pulmonary disease mild cognitive impairment Montreal AI ML |
| title | Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model |
| title_full | Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model |
| title_fullStr | Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model |
| title_full_unstemmed | Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model |
| title_short | Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model |
| title_sort | construction of a prediction and visualization system for cognitive impairment in elderly copd patients based on self assigning feature weights and residual evolution model |
| topic | chronic obstructive pulmonary disease mild cognitive impairment Montreal AI ML |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1473223/full |
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