دورية أكاديمية
Construction and Application of a Traditional Chinese Medicine Syndrome Differentiation Model for Dysmenorrhea Based on Machine Learning.
| العنوان: | Construction and Application of a Traditional Chinese Medicine Syndrome Differentiation Model for Dysmenorrhea Based on Machine Learning. |
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| المؤلفون: | Zhang L; College of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan Shanxi, China., You J; The Faculty of Applied Science and Engineering, University of Toronto, Toronto Ontario, Canada., Huang Y; Rotman Commerce, University of Toronto, Toronto Ontario, Canada., Jing R; The Faculty of Applied Science and Engineering, University of Toronto, Toronto Ontario, Canada., He Y; Rotman Commerce, University of Toronto, Toronto Ontario, Canada., Wen Y; College of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan Shanxi, China., Zheng L; College of Basic Medical, Shanxi University of Chinese Medicine, Taiyuan Shanxi, China., Zhao Y; College of Nursing, Shanxi University of Chinese Medicine, Taiyuan Shanxi, China. |
| المصدر: | Combinatorial chemistry & high throughput screening [Comb Chem High Throughput Screen] 2024 Feb 13. Date of Electronic Publication: 2024 Feb 13. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Bentham Science Publishers Country of Publication: United Arab Emirates NLM ID: 9810948 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1875-5402 (Electronic) Linking ISSN: 13862073 NLM ISO Abbreviation: Comb Chem High Throughput Screen Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Saif Zone, Sharjah, U.A.E. : Bentham Science Publishers Original Publication: Hilversum, Netherlands ; Miami, FL : Bentham Science Publishers, c1998- |
| مستخلص: | Background: Dysmenorrhea is one of the most common ailments affecting young and middle-aged women, significantly impacting their quality of life. Traditional Chinese Medicine (TCM) offers unique advantages in treating dysmenorrhea. However, an accurate diagnosis is essential to ensure correct treatment. This research integrates the age-old wisdom of TCM with modern Machine Learning (ML) techniques to enhance the precision and efficiency of dysmenorrhea syndrome differentiation, a pivotal process in TCM diagnostics and treatment planning. Methods: A total of 853 effective cases of dysmenorrhea were retrieved from the CNKI database, including patients' syndrome types, symptoms, and features, to establish the TCM information database of dysmenorrhea. Subsequently, 42 critical features were isolated from a potential set of 86 using a selection procedure augmented by Python's Scikit-Learn Library. Various machine learning models were employed, including Logistic Regression, Random Forest Classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN), each chosen for their potential to unearth complex patterns within the data. Results: Based on accuracy, precision, recall, and F1-score metrics, SVM emerged as the most effective model, showcasing an impressive precision of 98.29% and an accuracy of 98.24%. This model's analytical prowess not only highlighted the critical features pivotal to the syndrome differentiation process but also stands to significantly aid clinicians in formulating personalized treatment strategies by pinpointing nuanced symptoms with high precision. Conclusion: The study paves the way for a synergistic approach in TCM diagnostics, merging ancient wisdom with computational acuity, potentially innovating the diagnosis and treatment mode of TCM. Despite the promising outcomes, further research is needed to validate these models in real-world settings and extend this approach to other diseases addressed by TCM. (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.) |
| فهرسة مساهمة: | Keywords: dysmenorrhe; machine learning; support vector machine; traditional Chinese medicine diagnostic model |
| تواريخ الأحداث: | Date Created: 20240214 Latest Revision: 20240214 |
| رمز التحديث: | 20240214 |
| DOI: | 10.2174/0113862073293191240212091028 |
| PMID: | 38351686 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1875-5402 |
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| DOI: | 10.2174/0113862073293191240212091028 |