- Visibility 608 Views
- Downloads 598 Downloads
- Permissions
- DOI 10.18231/j.ijogr.v.12.i.3.29
-
CrossMark
- Citation
Retrospective analysis to predict preeclampsia by machine learning algorithms and its relation to neonatal outcome
- Author Details:
-
Sonal Dewangan
-
Mithlesh Dewangan *
Background: Preeclampsia is one of the most dangerous complications in pregnancy. It is characterised by hypertension, lower limb oedema, proteinuria, and thrombocytopenia. Preeclampsia can cause various complications that impact many body systems.
Aim and Objectives: The main aim of this study is to predict preeclampsia in mothers and find a possible relationship with neonatal outcomes.
Materials and Methods: This hospital-based retrospective case-control study involves 1231 pregnant women. They were randomly allocated into the testing (N=246) and training (N=985) groups, which were set at a ratio of 1:4. All medical records were analysed. Various machine learning models such as logistic regression (LR), K nearest neighbours (KNM), support vector machine (SVM), discriminant analysis (DA), extreme gradient boosting (EGB), and random forest (RF) algorithm were used to predict preeclampsia. The performance of these models was evaluated using standards such as accuracy, precision, recall, correct classification, misclassification, and F score using XLSTAT 2024.
Results: Out of the 1231 women, 368 were diagnosed with Preeclampsia. Factors such as kidney disease, blood pressure (BP) ≥ 120/80 mmHg in early pregnancy, chronic hypertension, family history of hypertension, and diabetes were found to be significant contributors to preeclampsia. The random forest model showed the highest performance.
Conclusions: Renal disease, chronic hypertension, family history of hypertension and diabetes and BP ≥120/80 mmHg early in pregnancy were the most important factors for predicting preeclampsia. The random forest model is the best feasible approach for screening and predicting preeclampsia because it helps obstetricians identify high-risk pregnancies and thus prevent adverse outcomes. Preeclampsia in mothers strongly affects neonatal outcomes, and this can result in a higher percentage of low birth weight, prematurity and intrauterine growth retardation.
References
- Rana S, Lemoine E, Granger JP, Karumanchi SA. Preeclampsia: pathophysiology, challenges, and perspectives. Circ Res. 2019;124(7):1094–112.
- Paller MS. Hypertension in pregnancy. J Am Soc Nephrol. 1998;9(2):314–21.
- Adorno M, Maher-Griffiths C, Grush Abadie HR. HELLP syndrome. Crit Care Nurs Clin North Am. 2022;34:277–88.
- Ghanaei MM, Afzali S, Morady A, Ghanaie R, Ghalebin S, Rafiei E, et al. Intrauterine growth restriction with and without pre- eclampsia: pregnancy outcome and placental findings. J Obstet Gynecol Cancer Res. 2022;7:177–85.
- Srinivas SK, Edlow AG, Neff PM, Sammel MD, Andrela CM, Elovitz MA. Rethinking IUGR in preeclampsia: dependent or independent of maternal hypertension? J Perinatol. 2009;29(10):680–4.
- Ying W, Catov JM, Ouyang P. Hypertensive disorders of pregnancy and future maternal cardiovascular risk. J Am Heart Assoc. 2018;7(17):e009382.
- Myatt L, Roberts JM. Preeclampsia: syndrome or disease? Curr Hypertens Rep. 2015;17(11):83.
- Cheikh Ismail L, Knight HE, Bhutta Z, Chumlea WC. Anthropometric protocols for the construction of new international fetal and newborn growth standards: the INTERGROWTH-21st Project. BJOG. 2013;120 Suppl 2(2):42–7.
- Ajah LO, Ozonu NC, Ezeonu PO, Lawani LO, Obuna JA, Onwe EO. The feto-maternal outcome of preeclampsia with severe features and eclampsia in Abakaliki, South-East Nigeria. J Clin Diagn Res. 2016;10(9):QC18–21.
- Musa J, Mohammed C, Ocheke A, Kahansim M, Pam V, Daru P. Incidence and risk factors for pre-eclampsia in Jos Nigeria. Afr Health Sci. 2018;18(3):584–95.
- Kim YR, Jung I, Heo SJ, Chang SW, Cho HY. A preeclampsia risk prediction model based on maternal characteristics and serum markers in twin pregnancy. J Matern Fetal Neonatal Med. 2021;34(21):3623–8.
- Townsend R, Khalil A, Premakumar Y, Allotey J, Snell KIE, Chan C, et al. Prediction of pre-eclampsia: review of reviews. Ultrasound Obstet Gynecol. 2019;54(1):16–27.
- Li S, Wang Z, Vieira LA, Zheutlin AB, Ru B, Schadt E, et al. Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data. NPJ Digit Med. 2022;5(1):68.
- Tang Z, Ji Y, Zhou S, Su T, Yuan Z, Han N, et al. Development and validation of multi-stage prediction models for pre-eclampsia: a retrospective cohort study on Chinese women. Front Public Health. 2022;10:911975.
- Boriboonhirunsarn D, Pradyachaipimol A, Viriyapak B. Incidence of superimposed preeclampsia among pregnant Asian women with chronic hypertension. Hypertens Pregnancy. 2017;36(2):226–31.
- Nie X, Xu Z, Ren H. Analysis of risk factors of preeclampsia in pregnant women with chronic hypertension and its impact on pregnancy outcomes. BMC Pregnancy Childbirth. 2024;24(1):307.
- Conti-Ramsden FI, Nathan HL, De Greeff A, Hall DR, Seed PT, Chappell LC, et al. Pregnancy-related acute kidney injury in preeclampsia: risk factors and renal outcomes. Hypertension. 2019;74(5):1144–51.
- Tangren JS, Wan Md Adnan WAH, Powe CE, Ecker J, Bramham K, Hladunewich MA, et al. Risk of preeclampsia and pregnancy complications in women with a history of acute kidney injury. Hypertension. 2018;72(2):451–9.
- Jhee JH, Lee S, Park Y, Lee SE, Kim YA, Kang SW, et al. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One. 2019;14(8):e0221202.
- Sutton EF, Rogan SC, Lopa S, Sharbaugh D, Muldoon MF, Catov JM. Early pregnancy blood pressure elevations and risk for maternal and neonatal morbidity. Obstet Gynecol. 2020;136(1):129–39.
- Sutton EF, Hauspurg A, Caritis SN, Powers RW, Catov JM. Maternal outcomes associated with lower range stage 1 hypertension. Obstet Gynecol. 2018;132(4):843–9.
- Hauspurg A, Sutton EF, Catov JM, Caritis SN. Aspirin effect on adverse pregnancy outcomes associated with stage 1 hypertension in a high-risk cohort. Hypertension. 2018;72(1):202–7.
- Hauspurg A, Parry S, Mercer BM, Grobman W, Hatfield T, Silver RM, et al. Blood pressure trajectory and category and risk of hypertensive disorders of pregnancy in nulliparous women. Am J Obstet Gynecol. 2019;221(3):277.e1–8.
- Qiu C, Williams MA, Leisenring WM, Sorensen TK, Frederick IO, Dempsey JC, et al. Family history of hypertension and type 2 diabetes in relation to preeclampsia risk. Hypertension. 2003;41(3):408–13.
- Skjaerven R, Vatten LJ, Wilcox AJ, Ronning T, Irgens LM, Lie RT. Recurrence of pre-eclampsia across generations: exploring fetal and maternal genetic components in a population based cohort. BMJ. 2005;331:877.
- Bezerra PC, Leão MD, Queiroz JW, Melo EM, Pereira FV, Nóbrega MH, et al. Family history of hypertension as an important risk factor for the development of severe preeclampsia. Acta Obstet Gynecol Scand. 2010;89(5):612–7.
- Phipps E, Prasanna D, Brima W, Jim B. Preeclampsia: updates in pathogenesis, definitions, and guidelines. Clin J Am Soc Nephrol. 2016;11(6):1102–13.
- Iriyama T, Wang W, Parchim NF, Sayama S, Kumasawa K, Nagamatsu T, et al. Reciprocal upregulation of hypoxia-inducible factor-1α and persistently enhanced placental adenosine signaling contribute to the pathogenesis of preeclampsia. FASEB J. 2020;34(3):4041–54.
- Chen X, Yuan L, Ji Z, Bian X, Hua S. Development and validation of the prediction models for preeclampsia: a retrospective, single- center, case-control study. Ann Transl Med. 2022; 10(22):1221.
- Martínez-Velasco A, Martínez-Villaseñor L, Miralles-Pechuán L. Machine learning approach for pre-eclampsia risk factors association. In: Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good techs ’18. 2018. p. 232–7.
- Liu M, Yang X, Chen G, Ding Y, Shi M, Sun L, et al. Development of a prediction model on preeclampsia using machine learning- based method: a retrospective cohort study in China. Front Physiol. 2022;13:896969.
- Li YX, Shen XP, Yang C, Cao ZZ, Du R, Yu M, et al. Novel electronic health records applied for prediction of pre-eclampsia: machine-learning algorithms. Pregnancy Hypertens. 2021;26:102–
- 546 Dewangan and Dewangan / Indian Journal of Obstetrics and Gynecology Research 2025;12(3):540–546
- Bosschieter TM, Xu Z, Lan H, Lengerich BJ, Nori H, Sitcov K, et al. Using interpretable machine learning to predict maternal and fetal outcomes [Preprint]. arXiv. 2022;arXiv:2207.05322.
- Carreno JF, Qiu P. Feature selection algorithms for predicting preeclampsia: a comparative approach. In: Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); 2020 Dec 16-19; Seoul, Republic of Korea. Piscataway (NJ): IEEE; 2020. p. 2626–31.
- Harmon QE, Huang L, Umbach DM, Klungsøyr K, Engel SM, Magnus P, et al. Risk of fetal death with preeclampsia. Obstet Gynecol. 2015;125(3):628–35.
- Maršál K. Preeclampsia and intrauterine growth restriction: placental disorders still not fully understood. J Perinat Med. 2017;45(7):775–7.
- Mateus J, Newman RB, Zhang C, Pugh SJ, Grewal J, Kim S, et al. Fetal growth patterns in pregnancy-associated hypertensive disorders: NICHD Fetal Growth Studies. Am J Obstet Gynecol. 2019;221(6):635.e1–16.
How to Cite This Article
Vancouver
Dewangan S, Dewangan M. Retrospective analysis to predict preeclampsia by machine learning algorithms and its relation to neonatal outcome [Internet]. Indian J Obstet Gynecol Res. 2025 [cited 2025 Oct 24];12(3):540-546. Available from: https://doi.org/10.18231/j.ijogr.v.12.i.3.29
APA
Dewangan, S., Dewangan, M. (2025). Retrospective analysis to predict preeclampsia by machine learning algorithms and its relation to neonatal outcome. Indian J Obstet Gynecol Res, 12(3), 540-546. https://doi.org/10.18231/j.ijogr.v.12.i.3.29
MLA
Dewangan, Sonal, Dewangan, Mithlesh. "Retrospective analysis to predict preeclampsia by machine learning algorithms and its relation to neonatal outcome." Indian J Obstet Gynecol Res, vol. 12, no. 3, 2025, pp. 540-546. https://doi.org/10.18231/j.ijogr.v.12.i.3.29
Chicago
Dewangan, S., Dewangan, M.. "Retrospective analysis to predict preeclampsia by machine learning algorithms and its relation to neonatal outcome." Indian J Obstet Gynecol Res 12, no. 3 (2025): 540-546. https://doi.org/10.18231/j.ijogr.v.12.i.3.29