Artificial intelligence-driven decision tree model for predicting quality of life determinants in women with endometriosis

Alexandre Vallée, Vallée A, Ayoubi JM, Jean-Marc Ayoubi, Jean‐Marc Ayoubi
International Journal of Gynecology & Obstetrics · 2025 · vol. 172(2) , pp. 1166–1175 · doi:10.1002/ijgo.70450 · PMID:40778483 · W4413050850
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This study developed and validated an AI decision tree model that identified progressive pain, dyspareunia, high BMI, infertility, and digestive symptoms as key predictors of reduced quality of life in women with endometriosis.

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This study developed an artificial intelligence-driven decision tree model to predict quality of life determinants in 1586 women with endometriosis across France. Using clinical, psychological, and sociodemographic variables such as pain progression, dyspareunia, body mass index, infertility, and digestive symptoms, the researchers trained and validated a classification system to distinguish between good and poor quality of life outcomes. The model demonstrated strong performance with an area under the curve of 0.75 in validation, identifying specific patient subgroups where progressive pain and high BMI were major predictors of poor quality of life. This paper is centrally about endometriosis — specifically focusing on using machine learning to stratify patients based on quality of life determinants.

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Abstract

OBJECTIVE: Endometriosis significantly impacts the quality of life (QoL) of affected women due to its complex symptomatology. This study aimed to develop a decision tree-based model to identify the key determinants influencing QoL in women with endometriosis, incorporating clinical, psychological, and sociodemographic factors. METHODS: A cross-sectional survey was conducted among 1586 women with endometriosis in France. QoL was assessed using the endometriosis health profile-5 (EHP-5) questionnaire, and key predictive variables included pain progression, dyspareunia, infertility, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) digestive symptoms, and educational level. A decision tree model was trained (70% of data) and validated (30%) to classify women into good or poor QoL categories, with performance evaluated using area under the curve (AUC), sensitivity, and specificity metrics. RESULTS: The decision tree model achieved an AUC of 0.88 (training) and 0.75 (validation), with precision (0.94), sensitivity (75%), and specificity (84%) in the training phase. The model identified 25 patient subgroups, where progressive pain, dyspareunia, high BMI, infertility, and digestive symptoms were major predictors of poor QoL (100% probability). Conversely, women with no pain progression, no dyspareunia, low BMI, and high education had a 0% probability of poor QoL. CONCLUSION: This study demonstrates the clinical utility of decision tree models in stratifying endometriosis patients based on QoL determinants. These findings support personalized treatment approaches, emphasizing pain management, fertility counseling, and lifestyle modifications. Future research should focus on AI integration into clinical decision tools for improved patient care.
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Abstract

Objective Endometriosis significantly impacts the quality of life (QoL) of affected women due to its complex symptomatology. This study aimed to develop a decision tree-based model to identify the key determinants influencing QoL in women with endometriosis, incorporating clinical, psychological, and sociodemographic factors.

Methods

A cross-sectional survey was conducted among 1586 women with endometriosis in France. QoL was assessed using the endometriosis health profile-5 (EHP-5) questionnaire, and key predictive variables included pain progression, dyspareunia, infertility, body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) digestive symptoms, and educational level. A decision tree model was trained (70% of data) and validated (30%) to classify women into good or poor QoL categories, with performance evaluated using area under the curve (AUC), sensitivity, and specificity metrics.

Results

The decision tree model achieved an AUC of 0.88 (training) and 0.75 (validation), with precision (0.94), sensitivity (75%), and specificity (84%) in the training phase. The model identified 25 patient subgroups, where progressive pain, dyspareunia, high BMI, infertility, and digestive symptoms were major predictors of poor QoL (100% probability). Conversely, women with no pain progression, no dyspareunia, low BMI, and high education had a 0% probability of poor QoL.

Conclusion

This study demonstrates the clinical utility of decision tree models in stratifying endometriosis patients based on QoL determinants. These findings support personalized treatment approaches, emphasizing pain management, fertility counseling, and lifestyle modifications. Future research should focus on AI integration into clinical decision tools for improved patient care. CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest for this research. DATA AVAILABILITY STATEMENT The data supporting the findings of this study are available upon reasonable request from the corresponding author. Due to ethical and privacy concerns, individual participant data cannot be shared publicly. Aggregated data and relevant analysis scripts are available upon request for academic and research purposes.

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Outcome instruments

EHP-30

Condition tags

dyspareuniaendometriosisinfertility

MeSH descriptors

Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence Artificial Intelligence

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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