Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires
Abstract Background Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and f...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | Reproductive Biology and Endocrinology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12958-023-01156-9 |
| _version_ | 1797647132052684800 |
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| author | Krystian Zieliński Dajana Drabczyk Michał Kunicki Damian Drzyzga Anna Kloska Jacek Rumiński |
| author_facet | Krystian Zieliński Dajana Drabczyk Michał Kunicki Damian Drzyzga Anna Kloska Jacek Rumiński |
| author_sort | Krystian Zieliński |
| collection | DOAJ |
| description | Abstract Background Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts’ decisions. As a benchmark model architecture, we utilized a LightGBM algorithm, along with Multivariate Imputation by Chained Equations (MICE) and k-nearest neighbors (KNN), for missing data imputation. Our primary objective was to assess the model’s performance and feature importance compared to existing studies. Results We identified the top 20 predictors of endometriosis, uncovering previously overlooked features such as Cesarean section, ovarian cysts, and hernia. Notably, the model’s performance metrics were maximized when utilizing a combination of multiple feature selection methods. Specifically, the final model achieved an area under the receiver operator characteristic curve (AUC) of 0.85 on the training dataset and an AUC of 0.82 on the testing dataset. Conclusions The application of machine learning in diagnosing endometriosis has the potential to significantly impact clinical practice, streamlining the diagnostic process and enhancing efficiency. Our questionnaire-based prediction approach empowers individuals with endometriosis to proactively identify potential symptoms, facilitating informed discussions with healthcare professionals about diagnosis and treatment options. |
| first_indexed | 2024-03-11T15:12:56Z |
| format | Article |
| id | doaj.art-932ba7b75a0b4628b97e077fffbab038 |
| institution | Directory Open Access Journal |
| issn | 1477-7827 |
| language | English |
| last_indexed | 2024-03-11T15:12:56Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Reproductive Biology and Endocrinology |
| spelling | doaj.art-932ba7b75a0b4628b97e077fffbab0382023-10-29T12:40:31ZengBMCReproductive Biology and Endocrinology1477-78272023-10-0121111310.1186/s12958-023-01156-9Evaluating the risk of endometriosis based on patients’ self-assessment questionnairesKrystian Zieliński0Dajana Drabczyk1Michał Kunicki2Damian Drzyzga3Anna Kloska4Jacek Rumiński5INVICTA, Research and Development CenterINVICTA, Research and Development CenterINVICTA, Research and Development CenterINVICTA, Research and Development CenterINVICTA, Research and Development CenterDepartment of Biomedical Engineering, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of TechnologyAbstract Background Endometriosis is a condition that significantly affects the quality of life of about 10 % of reproductive-aged women. It is characterized by the presence of tissue similar to the uterine lining (endometrium) outside the uterus, which can lead lead scarring, adhesions, pain, and fertility issues. While numerous factors associated with endometriosis are documented, a wide range of symptoms may still be undiscovered. Methods In this study, we employed machine learning algorithms to predict endometriosis based on the patient symptoms extracted from 13,933 questionnaires. We compared the results of feature selection obtained from various algorithms (i.e., Boruta algorithm, Recursive Feature Selection) with experts’ decisions. As a benchmark model architecture, we utilized a LightGBM algorithm, along with Multivariate Imputation by Chained Equations (MICE) and k-nearest neighbors (KNN), for missing data imputation. Our primary objective was to assess the model’s performance and feature importance compared to existing studies. Results We identified the top 20 predictors of endometriosis, uncovering previously overlooked features such as Cesarean section, ovarian cysts, and hernia. Notably, the model’s performance metrics were maximized when utilizing a combination of multiple feature selection methods. Specifically, the final model achieved an area under the receiver operator characteristic curve (AUC) of 0.85 on the training dataset and an AUC of 0.82 on the testing dataset. Conclusions The application of machine learning in diagnosing endometriosis has the potential to significantly impact clinical practice, streamlining the diagnostic process and enhancing efficiency. Our questionnaire-based prediction approach empowers individuals with endometriosis to proactively identify potential symptoms, facilitating informed discussions with healthcare professionals about diagnosis and treatment options.https://doi.org/10.1186/s12958-023-01156-9EndometriosisQuestionnaireMachine learningSymptom-based predictionFertility |
| spellingShingle | Krystian Zieliński Dajana Drabczyk Michał Kunicki Damian Drzyzga Anna Kloska Jacek Rumiński Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires Reproductive Biology and Endocrinology Endometriosis Questionnaire Machine learning Symptom-based prediction Fertility |
| title | Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires |
| title_full | Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires |
| title_fullStr | Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires |
| title_full_unstemmed | Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires |
| title_short | Evaluating the risk of endometriosis based on patients’ self-assessment questionnaires |
| title_sort | evaluating the risk of endometriosis based on patients self assessment questionnaires |
| topic | Endometriosis Questionnaire Machine learning Symptom-based prediction Fertility |
| url | https://doi.org/10.1186/s12958-023-01156-9 |
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