Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain
Abstract Background Mild cognitive impairment (MCI) is prevalent in older adults with chronic pain, making early detection crucial for dementia prevention and healthy aging. This study aimed to determine MCI risk factors in older patients with chronic pain and to develop 9 machine learning models to...
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BMC
2025-01-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-02723-8 |
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| author | Xiaoang Zhang Yuping Liao Daying Zhang Weichen Liu Zhijian Wang Yaxin Jin Shushu Chen Jianmei Wei |
| author_facet | Xiaoang Zhang Yuping Liao Daying Zhang Weichen Liu Zhijian Wang Yaxin Jin Shushu Chen Jianmei Wei |
| author_sort | Xiaoang Zhang |
| collection | DOAJ |
| description | Abstract Background Mild cognitive impairment (MCI) is prevalent in older adults with chronic pain, making early detection crucial for dementia prevention and healthy aging. This study aimed to determine MCI risk factors in older patients with chronic pain and to develop 9 machine learning models to identify MCI risk. Methods A total of 612 older patients with chronic pain were recruited between October 2023 and July 2024. Data collected included patients’ general information, cognitive function, pain level, depression, and sleep quality. The dataset was randomly divided into training set and testing set, and processed by Min-Max Normalization and SMOTETomek comprehensive sampling. SVM-RFE and LASSO regression were used for variable selection. We then developed machine learning models and interpreted them by SHAP. Results Age, education level, number of pain sites, pain duration, pain level, depression and sleep quality were risk factors of MCI in older patients with chronic pain. The Extreme Gradient Boosting (XGBoost) model performed best (AUC 0.925), with pain level, age, and depression as the most important variables. Conclusions We successfully developed 9 machine learning models to identify MCI risk. These models provide a tool for nurses to detect MCI risk early. We recommend that nurses integrate machine learning techniques into clinical nursing practice for managing MCI. However, these findings require validation with longitudinal data to confirm predictive validity for MCI progression. |
| format | Article |
| id | doaj-art-cd8b30c7b92b472cb12a662e787f9966 |
| institution | Kabale University |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-cd8b30c7b92b472cb12a662e787f99662025-01-26T12:22:52ZengBMCBMC Nursing1472-69552025-01-0124111410.1186/s12912-025-02723-8Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic painXiaoang Zhang0Yuping Liao1Daying Zhang2Weichen Liu3Zhijian Wang4Yaxin Jin5Shushu Chen6Jianmei Wei7School of Nursing, Jiangxi Medical College, Nanchang UniversityDepartment of Pain Medicine, the 1st affiliated hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Pain Medicine, the 1st affiliated hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Pain Medicine, the 1st affiliated hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Pain Medicine, the 1st affiliated hospital, Jiangxi Medical College, Nanchang UniversitySchool of Nursing, Jiangxi Medical College, Nanchang UniversitySchool of Nursing, Jiangxi Medical College, Nanchang UniversityDepartment of Pain Medicine, the 1st affiliated hospital, Jiangxi Medical College, Nanchang UniversityAbstract Background Mild cognitive impairment (MCI) is prevalent in older adults with chronic pain, making early detection crucial for dementia prevention and healthy aging. This study aimed to determine MCI risk factors in older patients with chronic pain and to develop 9 machine learning models to identify MCI risk. Methods A total of 612 older patients with chronic pain were recruited between October 2023 and July 2024. Data collected included patients’ general information, cognitive function, pain level, depression, and sleep quality. The dataset was randomly divided into training set and testing set, and processed by Min-Max Normalization and SMOTETomek comprehensive sampling. SVM-RFE and LASSO regression were used for variable selection. We then developed machine learning models and interpreted them by SHAP. Results Age, education level, number of pain sites, pain duration, pain level, depression and sleep quality were risk factors of MCI in older patients with chronic pain. The Extreme Gradient Boosting (XGBoost) model performed best (AUC 0.925), with pain level, age, and depression as the most important variables. Conclusions We successfully developed 9 machine learning models to identify MCI risk. These models provide a tool for nurses to detect MCI risk early. We recommend that nurses integrate machine learning techniques into clinical nursing practice for managing MCI. However, these findings require validation with longitudinal data to confirm predictive validity for MCI progression.https://doi.org/10.1186/s12912-025-02723-8Mild cognitive impairmentElderlyChronic painMachine learningPrediction modelSHAP |
| spellingShingle | Xiaoang Zhang Yuping Liao Daying Zhang Weichen Liu Zhijian Wang Yaxin Jin Shushu Chen Jianmei Wei Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain BMC Nursing Mild cognitive impairment Elderly Chronic pain Machine learning Prediction model SHAP |
| title | Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain |
| title_full | Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain |
| title_fullStr | Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain |
| title_full_unstemmed | Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain |
| title_short | Explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain |
| title_sort | explainable machine learning models for identifying mild cognitive impairment in older patients with chronic pain |
| topic | Mild cognitive impairment Elderly Chronic pain Machine learning Prediction model SHAP |
| url | https://doi.org/10.1186/s12912-025-02723-8 |
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