Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning
Insomnia is one of the most prevalent mental health disorders, and acupuncture has shown promise as an effective non-pharmacological treatment with minimal side effects. However, the response to acupuncture varies among individuals, emphasizing the need for personalized approaches to treatment. To a...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10918726/ |
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| author | Qingyun Wan Kai Liu Yuyang Bo Xiya Yuan Mufeng Li Xiaoqiu Wang Chuang Chen Lanying Liu Wenzhong Wu |
| author_facet | Qingyun Wan Kai Liu Yuyang Bo Xiya Yuan Mufeng Li Xiaoqiu Wang Chuang Chen Lanying Liu Wenzhong Wu |
| author_sort | Qingyun Wan |
| collection | DOAJ |
| description | Insomnia is one of the most prevalent mental health disorders, and acupuncture has shown promise as an effective non-pharmacological treatment with minimal side effects. However, the response to acupuncture varies among individuals, emphasizing the need for personalized approaches to treatment. To address this, an innovative machine learning algorithm, Relief-NDPGWO-WSVM, is developed to predict insomnia response to acupuncture. Data from 51 patients, considering 19 key factors such as age, sleep quality, anxiety level, and insomnia severity, were analyzed to identify the most influential predictors of treatment outcomes. The proposed model combines the Relief algorithm for feature selection, a weighted support vector machine (WSVM) to map these factors to treatment efficacy, and the NDPGWO optimization method, which incorporates a nonlinear convergence factor, dynamic weight, and probability perturbation. Experimental results demonstrate that this model outperforms existing models in terms of precision, recall, F1-score, accuracy, and area under the curve (AUC). These results highlight the model’s potential in accurately predicting acupuncture treatment outcomes for insomnia and suggest its broader applicability for enhancing clinical decision-making and optimizing healthcare resources for other related conditions. |
| format | Article |
| id | doaj-art-72b01198f0384854bbdb4f4d07e397d4 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-72b01198f0384854bbdb4f4d07e397d42025-08-20T03:02:55ZengIEEEIEEE Access2169-35362025-01-0113459644598410.1109/ACCESS.2025.354963010918726Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine LearningQingyun Wan0https://orcid.org/0000-0002-6189-8593Kai Liu1Yuyang Bo2Xiya Yuan3Mufeng Li4Xiaoqiu Wang5Chuang Chen6https://orcid.org/0000-0003-2117-7860Lanying Liu7Wenzhong Wu8https://orcid.org/0000-0002-9227-5671Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaShanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, ChinaAffiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaAffiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaAffiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaAffiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaCollege of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, ChinaAffiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaAffiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, ChinaInsomnia is one of the most prevalent mental health disorders, and acupuncture has shown promise as an effective non-pharmacological treatment with minimal side effects. However, the response to acupuncture varies among individuals, emphasizing the need for personalized approaches to treatment. To address this, an innovative machine learning algorithm, Relief-NDPGWO-WSVM, is developed to predict insomnia response to acupuncture. Data from 51 patients, considering 19 key factors such as age, sleep quality, anxiety level, and insomnia severity, were analyzed to identify the most influential predictors of treatment outcomes. The proposed model combines the Relief algorithm for feature selection, a weighted support vector machine (WSVM) to map these factors to treatment efficacy, and the NDPGWO optimization method, which incorporates a nonlinear convergence factor, dynamic weight, and probability perturbation. Experimental results demonstrate that this model outperforms existing models in terms of precision, recall, F1-score, accuracy, and area under the curve (AUC). These results highlight the model’s potential in accurately predicting acupuncture treatment outcomes for insomnia and suggest its broader applicability for enhancing clinical decision-making and optimizing healthcare resources for other related conditions.https://ieeexplore.ieee.org/document/10918726/Insomnia disorderacupuncturemachine learningefficacy evaluationswarm intelligence algorithm |
| spellingShingle | Qingyun Wan Kai Liu Yuyang Bo Xiya Yuan Mufeng Li Xiaoqiu Wang Chuang Chen Lanying Liu Wenzhong Wu Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning IEEE Access Insomnia disorder acupuncture machine learning efficacy evaluation swarm intelligence algorithm |
| title | Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning |
| title_full | Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning |
| title_fullStr | Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning |
| title_full_unstemmed | Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning |
| title_short | Predicting Insomnia Response to Acupuncture With the Development of Innovative Machine Learning |
| title_sort | predicting insomnia response to acupuncture with the development of innovative machine learning |
| topic | Insomnia disorder acupuncture machine learning efficacy evaluation swarm intelligence algorithm |
| url | https://ieeexplore.ieee.org/document/10918726/ |
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