⚡ Quick Summary
This study presents a comprehensive analysis of machine learning techniques to predict the risk of vaginal laxity (VL) in Chinese female patients. The XGBoost model demonstrated superior performance with an AUC of 0.9775, aiding in more accurate clinical diagnoses and personalized treatment plans.
🔍 Key Details
- 📊 Dataset: 1,580 cases including 1,184 women with VL and 396 healthy controls
- 🧩 Features used: Finger measurement method for VL categorization
- ⚙️ Technology: LightGBM, Random Forest, XGBoost, and AdaBoost models
- 🏆 Performance: XGBoost: AUC 0.9775, Accuracy 89.87%
🔑 Key Takeaways
- 📊 Predictive modeling can significantly enhance the diagnosis of vaginal laxity.
- 💡 Machine learning techniques were effectively applied to a large dataset of women.
- 👩🔬 The study involved 1,184 women with varying degrees of VL severity.
- 🏆 XGBoost outperformed other models in distinguishing between VL severity levels.
- 🤖 LightGBM and Random Forest also showed strong results with AUCs around 0.976.
- 🏥 Improved diagnostic accuracy can lead to personalized treatment plans and better patient outcomes.
- 🌍 Conducted in China, this study highlights the importance of machine learning in women’s health.
- 🆔 PMID: 39856162.
📚 Background
Vaginal laxity is a common condition that can significantly impact women’s pelvic floor health and quality of life. Traditional diagnostic methods may lack precision, leading to unnecessary treatments or surgeries. The integration of machine learning into clinical practice offers a promising avenue for improving diagnostic accuracy and treatment personalization.
🗒️ Study
This study aimed to develop predictive models for vaginal laxity among Chinese women using various machine learning techniques. A total of 1,580 participants were analyzed, including 1,184 women diagnosed with VL and 396 healthy controls. The researchers employed the finger measurement method to categorize the severity of VL and utilized advanced machine learning algorithms to analyze the data.
📈 Results
The results indicated that the XGBoost model achieved the highest performance with an AUC of 0.9775 and an overall accuracy of 89.87%. Other models, such as LightGBM and Random Forest, also performed well, each achieving an AUC of approximately 0.976. These findings suggest that machine learning can effectively differentiate between healthy individuals and those with varying degrees of VL.
🌍 Impact and Implications
The implications of this study are profound. By leveraging machine learning algorithms, healthcare providers can enhance their diagnostic capabilities, leading to more accurate assessments of vaginal laxity. This advancement not only aids in developing personalized treatment plans but also helps in reducing unnecessary surgical interventions, ultimately improving patient compliance and treatment outcomes.
🔮 Conclusion
This research underscores the transformative potential of machine learning in women’s health, particularly in the context of diagnosing vaginal laxity. The ability to accurately predict VL severity can lead to better clinical decisions and improved patient care. As we continue to explore the integration of AI in healthcare, the future looks promising for enhancing women’s pelvic floor health.
💬 Your comments
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Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity.
Abstract
This study develops predictive models for Chinese female patients with VL utilizing machine learning techniques. The aim is to create an effective model that can assist in clinical diagnosis and treatment of vaginal relaxation, thereby enhancing women’s pelvic floor health. In total, 1184 women with VL have been randomly selected and categorized into groups using the finger measurement method. Among them, there are 383 cases of mild VL, 405 cases of moderate VL, and 396 cases of severe VL. Concurrently, 396 healthy women without VL who underwent routine health examinations have been chosen at random and assigned to the non-VL group. Based on 1580 cases, we have established LightGBM, Random Forest, XGBoost, and AdaBoost models based on training dataset using 5-fold cross-validation and GridSearch, and analyzed the performance of the models on the hold-out test dataset. The confusion matrix, precision, recall, F1 score, overall accuracy, and ROC curve of the models on the hold-out test dataset are compared. The overall accuracy of LightGBM model, RF model, XGBoost model, and AdaBoost model are 0.8987, 0.8987, 0.8987, and 0.8457, respectively. The average AUC of LightGBM model is 0.976, the one of RF model is 0.9763, the one of XGBoost model is 0.9775, and the one of AdaBoost model is 0.928. The XGBoost model has the more comprehensive and reasonable performance among the four prediction models, which can accurately distinguish between healthy, mild VL, as well as moderate VL and severe VL, which can assist doctors in diagnosing persons’ conditions more accurately, devising personalized treatment plans, avoiding unnecessary surgeries, reducing persons’ psychological stress, improving patient compliance and treatment outcomes, thus enhancing overall treatment results.
Author: [‘Zhao H’, ‘Liu P’, ‘Chen F’, ‘Wang M’, ‘Liu J’, ‘Fu X’, ‘Yu H’, ‘Nai M’, ‘Li L’, ‘Li X’]
Journal: Sci Rep
Citation: Zhao H, et al. Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity. Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity. 2025; 15:3147. doi: 10.1038/s41598-025-86931-x