دورية أكاديمية
Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters.
| العنوان: | Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters. |
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| المؤلفون: | Yang J; Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: 305938765@qq.com., Zheng X; Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: 413479446@qq.com., Pan J; Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: 188795267@qq.com., Chen Y; Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: w31chenyumei@126.com., Chen C; Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: Cong1219@tom.com., Huang Z; Department of Obstetrics and Gynecology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou People's Hospital, Wenzhou, 325000, China. Electronic address: 195877893@qq.com. |
| المصدر: | Computers in biology and medicine [Comput Biol Med] 2024 Jul; Vol. 177, pp. 108599. Date of Electronic Publication: 2024 May 13. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York : Elsevier Original Publication: New York, Pergamon Press. |
| مواضيع طبية MeSH: | Support Vector Machine*, Humans ; Female ; Adult ; Tissue Adhesions ; Machine Learning ; Hysteroscopy/methods ; Uterine Diseases ; Severity of Illness Index ; Cryosurgery |
| مستخلص: | Intrauterine Adhesion (IUA) constitute a significant determinant impacting female fertility, potentially leading to infertility, miscarriage, menstrual irregularities, and placental complications. The precise assessment of the severity of IUA is pivotal for the customization of personalized treatment plans, aimed at enhancing the success rate of treatments and mitigating reproductive health risks. This study proposes bTLSMA-SVM-FS, a novel feature selection machine learning model that integrates an enhanced slime mould algorithm (SMA), termed TLSMA, with support vector machines (SVM), aiming to develop a predictive model for assessing the severity of IUA. Initially, a series of optimization comparative experiments were conducted on the TLSMA using the CEC 2017 benchmark functions. By comparing it with eleven meta-heuristic algorithms as well as eleven SOTA algorithms, the experimental outcomes corroborated the superior performance of the TLSMA. Subsequently, the developed bTLSMA-SVM-FS model was employed to conduct a thorough analysis of the clinical features of 107 IUA patients from Wenzhou People's Hospital, comprising 61 cases of moderate IUA and 46 cases of severe IUA. The evaluation results of the model demonstrated exceptional performance in predicting the severity of IUA, achieving an accuracy of 86.700 % and a specificity of 87.609 %. Moreover, the model successfully identified critical factors influencing the prediction of IUA severity, including the preoperative Chinese IUA score, production times, thrombin time, preoperative endometrial thickness, and menstruation. The identification of these key factors not only further validated the efficacy of the proposed model but also provided vital scientific evidence for a deeper understanding of the pathogenesis of IUA and the enhancement of targeted treatment strategies. Competing Interests: Declaration of competing interest The authors have declared that no competing interest exists. (Copyright © 2024. Published by Elsevier Ltd.) |
| فهرسة مساهمة: | Keywords: Feature selection; Intrauterine adhesion; Machine learning; Predictive modeling; Slime mould algorithm; Support vector machine |
| تواريخ الأحداث: | Date Created: 20240526 Date Completed: 20240611 Latest Revision: 20240611 |
| رمز التحديث: | 20240612 |
| DOI: | 10.1016/j.compbiomed.2024.108599 |
| PMID: | 38796878 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1879-0534 |
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| DOI: | 10.1016/j.compbiomed.2024.108599 |