Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA
TitleAdvanced Diagnosis and Forecasting of Pregnancy-Induced Hypertension in Obstetrics and Gynecology Education through the Integration of Genetic Algorithms.BackgroundPregnancy-induced hypertension represents a critical issue within the fields of obstetrics and gynecology, where precise diagnosis...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2024.1433479/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856956734963712 |
|---|---|
| author | Xiaolan Li Xiaolan Li Xiaolan Li Fen Kang Fen Kang Fen Kang Xiaojing Li Xiaojing Li Xiaojing Li |
| author_facet | Xiaolan Li Xiaolan Li Xiaolan Li Fen Kang Fen Kang Fen Kang Xiaojing Li Xiaojing Li Xiaojing Li |
| author_sort | Xiaolan Li |
| collection | DOAJ |
| description | TitleAdvanced Diagnosis and Forecasting of Pregnancy-Induced Hypertension in Obstetrics and Gynecology Education through the Integration of Genetic Algorithms.BackgroundPregnancy-induced hypertension represents a critical issue within the fields of obstetrics and gynecology, where precise diagnosis and forecasting are essential for effective management. The potential for misdiagnosis, often stemming from the inexperience of healthcare professionals, underscores the necessity for an advanced diagnostic system.MethodsThis research introduces an innovative sampling and feature selection technique grounded in F-scores optimization, alongside the development of a comprehensive prediction model that integrates genetic algorithms with various heterogeneous learners. The objective of this model is to maximize the utility of medical data and enhance treatment quality.ResultsThe refined intelligent feature selection approach identified several significant indicators of pregnancy-related hypertension, such as phosphor dehydrogenase deficiency, body mass index, gestational urinary proteins, vascular endothelial growth factor receptor 1, placental growth factor, thalassemia, and a familial history of diabetes mellitus or hypertension. The model achieved superior performance metrics, including the highest recall (0.768), F-score (0.728), and area under the curve (0.832) when compared to other prevalent models. Furthermore, the area under the curve for both early and late clinical assessments reached peak values of 0.996 and 0.792, respectively, when evaluated using the ratio of vascular endothelial growth factor receptor 1 to placental growth factor.ConclusionThe intelligent diagnosis and prediction methodology for gestational hypertension proposed in this study exhibited remarkable efficacy and holds significant promise for implementation in both educational and clinical settings within obstetrics and gynecology, thereby advancing intelligent medical diagnostics in China. |
| format | Article |
| id | doaj-art-0e6f0bb1d7e6433791465d4977c0fccc |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-0e6f0bb1d7e6433791465d4977c0fccc2025-02-12T04:11:06ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-02-011110.3389/fmed.2024.14334791433479Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GAXiaolan Li0Xiaolan Li1Xiaolan Li2Fen Kang3Fen Kang4Fen Kang5Xiaojing Li6Xiaojing Li7Xiaojing Li8Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaNHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract (Anhui Medical University), Hefei, ChinaKey Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People’s Republic of China, Hefei, ChinaDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaNHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract (Anhui Medical University), Hefei, ChinaKey Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People’s Republic of China, Hefei, ChinaDepartment of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, ChinaAnhui Province Key Laboratory of Reproductive Health and Genetics, Hefei, ChinaAnhui Provincial Engineering Research Center of Biopreservation and Artificial Organs, Hefei, ChinaTitleAdvanced Diagnosis and Forecasting of Pregnancy-Induced Hypertension in Obstetrics and Gynecology Education through the Integration of Genetic Algorithms.BackgroundPregnancy-induced hypertension represents a critical issue within the fields of obstetrics and gynecology, where precise diagnosis and forecasting are essential for effective management. The potential for misdiagnosis, often stemming from the inexperience of healthcare professionals, underscores the necessity for an advanced diagnostic system.MethodsThis research introduces an innovative sampling and feature selection technique grounded in F-scores optimization, alongside the development of a comprehensive prediction model that integrates genetic algorithms with various heterogeneous learners. The objective of this model is to maximize the utility of medical data and enhance treatment quality.ResultsThe refined intelligent feature selection approach identified several significant indicators of pregnancy-related hypertension, such as phosphor dehydrogenase deficiency, body mass index, gestational urinary proteins, vascular endothelial growth factor receptor 1, placental growth factor, thalassemia, and a familial history of diabetes mellitus or hypertension. The model achieved superior performance metrics, including the highest recall (0.768), F-score (0.728), and area under the curve (0.832) when compared to other prevalent models. Furthermore, the area under the curve for both early and late clinical assessments reached peak values of 0.996 and 0.792, respectively, when evaluated using the ratio of vascular endothelial growth factor receptor 1 to placental growth factor.ConclusionThe intelligent diagnosis and prediction methodology for gestational hypertension proposed in this study exhibited remarkable efficacy and holds significant promise for implementation in both educational and clinical settings within obstetrics and gynecology, thereby advancing intelligent medical diagnostics in China.https://www.frontiersin.org/articles/10.3389/fmed.2024.1433479/fullobstetrics and gynecology teachinggenetic algorithmpregnancy induced hypertensionintelligent diagnosisfeature selection |
| spellingShingle | Xiaolan Li Xiaolan Li Xiaolan Li Fen Kang Fen Kang Fen Kang Xiaojing Li Xiaojing Li Xiaojing Li Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA Frontiers in Medicine obstetrics and gynecology teaching genetic algorithm pregnancy induced hypertension intelligent diagnosis feature selection |
| title | Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA |
| title_full | Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA |
| title_fullStr | Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA |
| title_full_unstemmed | Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA |
| title_short | Intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating GA |
| title_sort | intelligent diagnosis and prediction of pregnancy induced hypertension in obstetrics and gynecology teaching by integrating ga |
| topic | obstetrics and gynecology teaching genetic algorithm pregnancy induced hypertension intelligent diagnosis feature selection |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2024.1433479/full |
| work_keys_str_mv | AT xiaolanli intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT xiaolanli intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT xiaolanli intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT fenkang intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT fenkang intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT fenkang intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT xiaojingli intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT xiaojingli intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga AT xiaojingli intelligentdiagnosisandpredictionofpregnancyinducedhypertensioninobstetricsandgynecologyteachingbyintegratingga |