Construction of a prediction model for induction of labor based on a small sample of clinical indicator data_Journal of Biomedical Engineering

Authors：

QIN Yali ¹ , YAO Liping ² , YUAN Ling ³ ,  CHEN Sheng ¹

1. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Department of Ultrasound, Shanghai First Maternity and Infant Health Hospital, Shanghai 201204, P. R. China;
3. Department of Obstetrics, Shanghai First Maternity and Infant Health Hospital, Shanghai 201204, P. R. China;

Corresponding author：

CHEN Sheng, Email: chnshn@163.com

Keywords：

Induction of labor; Maximal information coefficient; Support vector machine; Fully connected neural network; Predictive model; Overfitting

DOI：

10.7507/1001-5515.202403033

Video：

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Because of the diversity and complexity of clinical indicators, it is difficult to establish a comprehensive and reliable prediction model for induction of labor (IOL) outcomes with existing methods. This study aims to analyze the clinical indicators related to IOL and to develop and evaluate a prediction model based on a small-sample of data. The study population consisted of a total of 90 pregnant women who underwent IOL between February 2023 and January 2024 at the Shanghai First Maternity and Infant Healthcare Hospital, and a total of 52 clinical indicators were recorded. Maximal information coefficient (MIC) was used to select features for clinical indicators to reduce the risk of overfitting caused by high-dimensional features. Then, based on the features selected by MIC, the support vector machine (SVM) model based on small samples was compared and analyzed with the fully connected neural network (FCNN) model based on large samples in deep learning, and the receiver operating characteristic (ROC) curve was given. By calculating the MIC score, the final feature dimension was reduced from 55 to 15, and the area under curve (AUC) of the SVM model was improved from 0.872 before feature selection to 0.923. Model comparison results showed that SVM had better prediction performance than FCNN. This study demonstrates that SVM successfully predicted IOL outcomes, and the MIC feature selection effectively improves the model’s generalization ability, making the prediction results more stable. This study provides a reliable method for predicting the outcome of induced labor with potential clinical applications.

Citation： QIN Yali, YAO Liping, YUAN Ling, CHEN Sheng. Construction of a prediction model for induction of labor based on a small sample of clinical indicator data. Journal of Biomedical Engineering, 2024, 41(5): 1012-1018. doi: 10.7507/1001-5515.202403033 Copy

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3.	Osterman M, Hamilton B, Martin J A, et al. Births: Final data for 2020. Natl Vital Stat Rep, 2021, 70(17): 1-50.
4.	Wang L, Wang G, Cao W, et al. Comparison of the cook vaginal cervical ripening balloon with prostaglandin E2 insert for induction of labor in late pregnancy. Arch Gynecol Obstet, 2020, 302(3): 579-584.
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10.	Hu T, Du S, Li X, et al. Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm. Sci Rep, 2022, 12(1): 19063.
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12.	Hwang H S, Sohn I S, Kwon H S. Imaging analysis of cervical elastography for prediction of successful induction of labor at term. J Ultrasound Med, 2013, 32(6): 937-946.
13.	Caughey A B, Cahill A G, Guise J-M, et al. Safe prevention of the primary cesarean delivery. Am J Obstet Gynecol, 2014, 210(3): 179-193.
14.	Lu J, Lu J, Cheng Y K Y, et al. The predictive value of cervical shear wave elastography in the outcome of labor induction. Acta Obstet Gynecol Scand, 2019, 99: 59-68.
15.	Altman N, Krzywinski M. The curse(s) of dimensionality. Nat Methods, 2018, 15(6): 399-400.
16.	Yang B, Xu Y, Maxwell A, et al. MICRAT: A novel algorithm for inferring gene regulatory networks using time series gene expression data. BMC Syst Biol, 2018, 12(Suppl 7): 115.
17.	Reshef D N, Reshef Y A, Finucane H K, et al. Detecting novel associations in large data sets. Science, 2011, 334: 1518-1524.
18.	王玉潇, 姜威, 刘治, 等. 基于共空间模式算法和支持向量机二重分类的癫痫发病预测. 生物医学工程学杂志, 2021, 38(1): 39-46.
19.	Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, et al. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 2020, 408: 189-215.
20.	Taud H, Mas J F. Geomatic approaches for modeling land change scenarios. Cham: Springer International Publishing, 2018: 451-455.
21.	刘美君, 吴全玉, 丁胜, 等. 自适应噪声完备经验模态分解排列熵结合支持向量机的心音分类方法研究. 生物医学工程学杂志, 2022, 39(2): 311-319.
22.	Crane J M G. Factors predicting labor induction success: A critical analysis. Clin Obstet Gynecol, 2006, 49: 573-584.
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24.	刘千祺, 陈俊雅. 超声指标预测引产结局的研究进展. 中国临床医学影像杂志, 2022, 33(7): 519-522.
25.	Yang Q, Zhou C C, Chen Y, et al. Prediction model for successful induction of labor by cervical strain elastography diagnosed at late-term pregnancy in nulliparous women: A prospective cohort study. BMC Pregnancy Childbirth, 2023, 23(1): 114.
26.	Ellis J A, Brown C M, Barger B, et al. Influence of maternal obesity on labor induction: A systematic review and meta-analysis. J Midwifery Womens Health, 2019, 64(1): 55-67.

1. Penfield C A, Wing D A. Labor induction techniques: Which is the best? Obstet Gynecol Clin North Am, 2017, 44(4): 567-582.
2. Panelli D M, Robinson J N, Kaimal A J, et al. Using cervical dilation to predict labor onset: A tool for elective labor induction counseling. Am J Perinatol, 2019, 36: 1485-1491.
3. Osterman M, Hamilton B, Martin J A, et al. Births: Final data for 2020. Natl Vital Stat Rep, 2021, 70(17): 1-50.
4. Wang L, Wang G, Cao W, et al. Comparison of the cook vaginal cervical ripening balloon with prostaglandin E2 insert for induction of labor in late pregnancy. Arch Gynecol Obstet, 2020, 302(3): 579-584.
5. Muglu J, Rather H, Arroyo-Manzano D, et al. Risks of stillbirth and neonatal death with advancing gestation at term: A systematic review and meta-analysis of cohort studies of 15 million pregnancies. PLoS Med, 2019, 16(7): e1002838.
6. Wu P, Green M, Myers J E. Hypertensive disorders of pregnancy. BMJ, 2023, 381: e071653.
7. Shehab M, Abualigah L M, Shambour Q Y, et al. Machine learning in medical applications: A review of state-of-the-art methods. Comput Biol Med, 2022, 145: 105458.
8. D'souza R, Ashraf R, Foroutan F. Prediction models for determining the success of labour induction: A systematic review and critical analysis. Best Pract Res Clin Obstet Gynaecol, 2022, 79: 42-54.
9. Perelman A, Marty L, Hirschberg C I, et al. Progression of hypertensive disorders of pregnancy during induction of labor at term. Am J Obstet Gynecol, 2022, 226(1): S503.
10. Hu T, Du S, Li X, et al. Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm. Sci Rep, 2022, 12(1): 19063.
11. Huang K, Liu Z, Luo J, et al. Random forest model for labor induction in pregnant women with hypertensive disorders using a cervical double balloon. Altern Ther Health Med, 2023, 29(1): 44-51.
12. Hwang H S, Sohn I S, Kwon H S. Imaging analysis of cervical elastography for prediction of successful induction of labor at term. J Ultrasound Med, 2013, 32(6): 937-946.
13. Caughey A B, Cahill A G, Guise J-M, et al. Safe prevention of the primary cesarean delivery. Am J Obstet Gynecol, 2014, 210(3): 179-193.
14. Lu J, Lu J, Cheng Y K Y, et al. The predictive value of cervical shear wave elastography in the outcome of labor induction. Acta Obstet Gynecol Scand, 2019, 99: 59-68.
15. Altman N, Krzywinski M. The curse(s) of dimensionality. Nat Methods, 2018, 15(6): 399-400.
16. Yang B, Xu Y, Maxwell A, et al. MICRAT: A novel algorithm for inferring gene regulatory networks using time series gene expression data. BMC Syst Biol, 2018, 12(Suppl 7): 115.
17. Reshef D N, Reshef Y A, Finucane H K, et al. Detecting novel associations in large data sets. Science, 2011, 334: 1518-1524.
18. 王玉潇, 姜威, 刘治, 等. 基于共空间模式算法和支持向量机二重分类的癫痫发病预测. 生物医学工程学杂志, 2021, 38(1): 39-46.
19. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L, et al. A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 2020, 408: 189-215.
20. Taud H, Mas J F. Geomatic approaches for modeling land change scenarios. Cham: Springer International Publishing, 2018: 451-455.
21. 刘美君, 吴全玉, 丁胜, 等. 自适应噪声完备经验模态分解排列熵结合支持向量机的心音分类方法研究. 生物医学工程学杂志, 2022, 39(2): 311-319.
22. Crane J M G. Factors predicting labor induction success: A critical analysis. Clin Obstet Gynecol, 2006, 49: 573-584.
23. Teixeira C, Lunet N, Rodrigues T, et al. The Bishop Score as a determinant of labour induction success: a systematic review and meta-analysis. Arch Gynecol Obstet, 2012, 286(3): 739-53.
24. 刘千祺, 陈俊雅. 超声指标预测引产结局的研究进展. 中国临床医学影像杂志, 2022, 33(7): 519-522.
25. Yang Q, Zhou C C, Chen Y, et al. Prediction model for successful induction of labor by cervical strain elastography diagnosed at late-term pregnancy in nulliparous women: A prospective cohort study. BMC Pregnancy Childbirth, 2023, 23(1): 114.
26. Ellis J A, Brown C M, Barger B, et al. Influence of maternal obesity on labor induction: A systematic review and meta-analysis. J Midwifery Womens Health, 2019, 64(1): 55-67.

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