Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ndefo, U.A.; Eaton, A.; Green, M.R. Polycystic ovary syndrome: A review of treatment options with a focus on pharmacological approaches. Pharm. Ther. 2013, 38, 336–355. [Google Scholar]
- Joham, A.E.; Norman, R.J.; Stener-Victorin, E.; Legro, R.S.; Franks, S.; Moran, L.J.; Boyle, J.; Teede, H.J. Polycystic ovary syndrome. Lancet Diabetes Endocrinol. 2022, 10, 668–680. [Google Scholar] [CrossRef]
- Li, Y.; Chen, C.; Ma, Y.; Xiao, J.; Luo, G.; Li, Y.; Wu, D. Multi-system reproductive metabolic disorder: Significance for the pathogenesis and therapy of polycystic ovary syndrome (PCOS). Life Sci. 2019, 228, 167–175. [Google Scholar] [CrossRef]
- Boomsma, C.; Eijkemans, M.; Hughes, E.; Visser, G.; Fauser, B.; Macklon, N. A meta-analysis of pregnancy outcomes in women with polycystic ovary syndrome. Hum. Reprod. Update 2006, 12, 673–683. [Google Scholar] [CrossRef] [PubMed]
- Kjerulff, L.E.; Sanchez-Ramos, L.; Duffy, D. Pregnancy outcomes in women with polycystic ovary syndrome: A metaanalysis. Am. J. Obstet. Gynecol. 2011, 204, 558.e1–558.e6. [Google Scholar] [CrossRef] [PubMed]
- Qin, J.Z.; Pang, L.H.; Li, M.J.; Fan, X.J.; Huang, R.D.; Chen, H.Y. Obstetric complications in women with polycystic ovary syndrome: A systematic review and meta-analysis. Reprod. Biol. Endocrinol. 2013, 11, 56. [Google Scholar] [CrossRef]
- Cooney, L.G.; Dokras, A. Beyond fertility: Polycystic ovary syndrome and long-term health. Fertil. Steril. 2018, 110, 794–809. [Google Scholar] [CrossRef] [PubMed]
- Fauser, B.C.; Tarlatzis, B.C.; Rebar, R.W.; Legro, R.S.; Balen, A.H.; Lobo, R.; Carmina, E.; Chang, J.; Yildiz, B.O.; Laven, J.S.; et al. Consensus on women’s health aspects of polycystic ovary syndrome (PCOS): The Amsterdam ESHRE/ASRM-Sponsored 3rd PCOS Consensus Workshop Group. Fertil. Steril. 2012, 97, 28–38.e25. [Google Scholar] [CrossRef]
- Lizneva, D.; Suturina, L.; Walker, W.; Brakta, S.; Gavrilova-Jordan, L.; Azziz, R. Criteria, prevalence, and phenotypes of polycystic ovary syndrome. Fertil. Steril. 2016, 106, 6–15. [Google Scholar] [CrossRef]
- Siddiqui, S.; Mateen, S.; Ahmad, R.; Moin, S. A brief insight into the etiology, genetics, and immunology of polycystic ovarian syndrome (PCOS). J. Assist. Reprod. Genet. 2022, 39, 2439–2473. [Google Scholar] [CrossRef]
- Elasam, A.N.; Ahmed, M.A.; Ahmed, A.B.A.; Sharif, M.E.; Abusham, A.; Hassan, B.; Adam, I. The prevalence and phenotypic manifestations of polycystic ovary syndrome (PCOS) among infertile Sudanese women: A cross-sectional study. BMC Womens Health 2022, 22, 165. [Google Scholar] [CrossRef] [PubMed]
- Mills, G.; Badeghiesh, A.; Suarthana, E.; Baghlaf, H.; Dahan, M.H. Associations between polycystic ovary syndrome and adverse obstetric and neonatal outcomes: A population study of 9.1 million births. Hum. Reprod. 2020, 35, 1914–1921. [Google Scholar] [CrossRef] [PubMed]
- Vasilache, I.A.; Scripcariu, I.S.; Doroftei, B.; Bernad, R.L.; Cărăuleanu, A.; Socolov, D.; Melinte-Popescu, A.S.; Vicoveanu, P.; Harabor, V.; Mihalceanu, E.; et al. Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study. Diagnostics 2024, 14, 453. [Google Scholar] [CrossRef] [PubMed]
- Melinte-Popescu, M.; Vasilache, I.A.; Socolov, D.; Melinte-Popescu, A.S. Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms-Results from a Retrospective Study. Diagnostics 2023, 13, 287. [Google Scholar] [CrossRef] [PubMed]
- Melinte-Popescu, A.-S.; Vasilache, I.-A.; Socolov, D.; Melinte-Popescu, M. Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia—A Prospective Study. J. Clin. Med. 2023, 12, 418. [Google Scholar] [CrossRef] [PubMed]
- Ursuleanu, T.F.; Luca, A.R.; Gheorghe, L.; Grigorovici, R.; Iancu, S.; Hlusneac, M.; Preda, C.; Grigorovici, A. Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images. Diagnostics 2021, 11, 1373. [Google Scholar] [CrossRef] [PubMed]
- Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum. Reprod. 2004, 19, 41–47. [Google Scholar]
- American Diabetes Association Professional Practice Committee; ElSayed, N.A.; Aleppo, G.; Bannuru, R.R.; Bruemmer, D.; Collins, B.S.; Ekhlaspour, L.; Gaglia, J.L.; Hilliard, M.E.; Johnson, E.L.; et al. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care 2024, 47, S20–S42. [Google Scholar] [CrossRef]
- Metzger, B.E.; Gabbe, S.G.; Persson, B.; Buchanan, T.A.; Catalano, P.A.; Damm, P.; Dyer, A.R.; Leiva, A.; Hod, M.; Kitzmiler, J.L.; et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 2010, 33, 676–682. [Google Scholar] [CrossRef]
- WHO Consultation. Obesity: Preventing and managing the global epidemic. World Health Organ. Tech. Rep. Ser. 2000, 894, 1–253. [Google Scholar]
- Ooi, M.P.-L.; Sok, H.K.; Kuang, Y.C.; Demidenko, S. Chapter 19—Alternating Decision Trees. In Handbook of Neural Computation; Samui, P., Sekhar, S., Balas, V.E., Eds.; Academic Press: Cambridge, MA, USA, 2017; pp. 345–371. [Google Scholar]
- Uddin, S.; Khan, A.; Hossain, M.E.; Moni, M.A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 2019, 19, 281. [Google Scholar] [CrossRef] [PubMed]
- Lee, Y.W.; Choi, J.W.; Shin, E.H. Machine learning model for predicting malaria using clinical information. Comput. Biol. Med. 2021, 129, 104151. [Google Scholar] [CrossRef]
- Ye, Y.; Xiong, Y.; Zhou, Q.; Wu, J.; Li, X.; Xiao, X. Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study. J. Diabetes Res. 2020, 2020, 4168340. [Google Scholar] [CrossRef]
- Gupta, A.; Katarya, R. Social media based surveillance systems for healthcare using machine learning: A systematic review. J. Biomed. Inform. 2020, 108, 103500. [Google Scholar] [CrossRef]
- Yang, R.; Li, Q.; Zhou, Z.; Qian, W.; Zhang, J.; Wu, Z.; Jin, L.; Wu, X.; Zhang, C.; Zheng, B.; et al. Changes in the prevalence of polycystic ovary syndrome in China over the past decade. Lancet Reg. Health West. Pac. 2022, 25, 100494. [Google Scholar] [CrossRef] [PubMed]
- AlAhmari, L.S.M.; Alzahrani, H.S.; Alzahrani, N.; AlDhafyan, S.O.; Al-Qahtani, R.H.; Al-Zaid, J.A.; AlShahrani, M.S.; Al-Ghamdi, L.A. Measures of health-related quality of life in PCOS women: A cross sectional study from Saudi Arabia. Eur. Rev. Med. Pharmacol. Sci. 2024, 28, 1913–1919. [Google Scholar] [CrossRef] [PubMed]
- Damone, A.L.; Joham, A.E.; Loxton, D.; Earnest, A.; Teede, H.J.; Moran, L.J. Depression, anxiety and perceived stress in women with and without PCOS: A community-based study. Psychol. Med. 2019, 49, 1510–1520. [Google Scholar] [CrossRef]
- Xu, Y.; Qiao, J. Association of Insulin Resistance and Elevated Androgen Levels with Polycystic Ovarian Syndrome (PCOS): A Review of Literature. J. Healthc. Eng. 2022, 2022, 9240569. [Google Scholar] [CrossRef]
- Lim, S.S.; Kakoly, N.S.; Tan, J.W.J.; Fitzgerald, G.; Bahri Khomami, M.; Joham, A.E.; Cooray, S.D.; Misso, M.L.; Norman, R.J.; Harrison, C.L.; et al. Metabolic syndrome in polycystic ovary syndrome: A systematic review, meta-analysis and meta-regression. Obes. Rev. 2019, 20, 339–352. [Google Scholar] [CrossRef]
- Zehravi, M.; Maqbool, M.; Ara, I. Polycystic ovary syndrome and infertility: An update. Int. J. Adolesc. Med. Health 2021, 34, 1–9. [Google Scholar] [CrossRef]
- Roos, N.; Kieler, H.; Sahlin, L.; Ekman-Ordeberg, G.; Falconer, H.; Stephansson, O. Risk of adverse pregnancy outcomes in women with polycystic ovary syndrome: Population based cohort study. Bmj 2011, 343, d6309. [Google Scholar] [CrossRef] [PubMed]
- Palomba, S.; Falbo, A.; Chiossi, G.; Orio, F.; Tolino, A.; Colao, A.; La Sala, G.B.; Zullo, F. Low-grade chronic inflammation in pregnant women with polycystic ovary syndrome: A prospective controlled clinical study. J. Clin. Endocrinol. Metab. 2014, 99, 2942–2951. [Google Scholar] [CrossRef] [PubMed]
- Liu, Q.; Wang, J.; Xu, Q.; Kong, L.; Wang, J. A retrospective cohort study of obstetric complications and birth outcomes in women with polycystic ovarian syndrome. J. Obstet. Gynaecol. 2022, 42, 574–579. [Google Scholar] [CrossRef] [PubMed]
- Choudhury, A.A.; Rajeswari, V.D. Polycystic ovary syndrome (PCOS) increases the risk of subsequent gestational diabetes mellitus (GDM): A novel therapeutic perspective. Life Sci. 2022, 310, 121069. [Google Scholar] [CrossRef] [PubMed]
- Galesanu, C.; Buzduga, C.; Florescu, A.; Moisii, L.; Ciubotaru, V. Diabetes mellitus, chronic complication in patients with acromegaly: Case report and review of the literature. Med.-Surg. J. 2015, 119, 92–96. [Google Scholar]
- Hanna, F.; Wu, P.; Heald, A.; Fryer, A. Diabetes detection in women with gestational diabetes and polycystic ovarian syndrome. Bmj 2023, 382, e071675. [Google Scholar] [CrossRef] [PubMed]
- Ward, R.J.; Fryer, A.A.; Hanna, F.W.; Spencer, N.; Mahmood, M.; Wu, P.; Heald, A.H.; Duff, C.J. Inadequate postpartum screening for type 2 diabetes in women with previous gestation diabetes mellitus: A retrospective audit of practice over 17 years. Int. J. Clin. Pract. 2021, 75, e14447. [Google Scholar] [CrossRef] [PubMed]
- Sebastian, M.R.; Wiemann, C.M.; Bacha, F.; Taylor, S.J.A. Diagnostic evaluation, comorbidity screening, and treatment of polycystic ovary syndrome in adolescents in 3 specialty clinics. J. Pediatr. Adolesc. Gynecol. 2018, 31, 367–371. [Google Scholar] [CrossRef] [PubMed]
- Vanky, E.; Stridsklev, S.; Heimstad, R.; Romundstad, P.l.; Skogøy, K.; Kleggetveit, O.; Hjelle, S.; von Brandis, P.; Eikeland, T.; Flo, K. Metformin versus placebo from first trimester to delivery in polycystic ovary syndrome: A randomized, controlled multicenter study. J. Clin. Endocrinol. Metab. 2010, 95, E448–E455. [Google Scholar] [CrossRef]
- Kanda, S.; Chatha, U.; Odoma, V.A.; Pitliya, A.; AlEdani, E.M.; Bhangu, J.K.; Javed, K.; Manshahia, P.K.; Nahar, S.; Hamid, P. Effect of Metformin (MTF) Intervention During Pregnancy in Women With Polycystic Ovarian Syndrome (PCOS): A Systematic Review. Cureus 2023, 15, e44166. [Google Scholar] [CrossRef]
- Bahri Khomami, M.; Joham, A.E.; Boyle, J.A.; Piltonen, T.; Arora, C.; Silagy, M.; Misso, M.L.; Teede, H.J.; Moran, L.J. The role of maternal obesity in infant outcomes in polycystic ovary syndrome—A systematic review, meta-analysis, and meta-regression. Obes. Rev. 2019, 20, 842–858. [Google Scholar] [CrossRef] [PubMed]
- Barrera, F.J.; Brown, E.D.L.; Rojo, A.; Obeso, J.; Plata, H.; Lincango, E.P.; Terry, N.; Rodríguez-Gutiérrez, R.; Hall, J.E.; Shekhar, S. Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: A systematic review. Front. Endocrinol. 2023, 14, 1106625. [Google Scholar] [CrossRef] [PubMed]
- Zhou, F.; Xing, Y.; Cheng, T.; Yang, L.; Ma, H. Exploration of hub genes involved in PCOS using biological informatics methods. Medicine 2022, 101, e30905. [Google Scholar] [CrossRef] [PubMed]
Clinical Characteristics | PCOS Group (n = 87 Patients) | Control Group (n = 87 Patients) | p Value |
---|---|---|---|
Age, years (mean ± SD) | 29.6 ± 6.19 | 26.12 ± 5.74 | 0.52 |
IVF conception (n/%) | Yes = 18 (20.68%) No = 69 (79.32%) | Yes = 7 (8.04%) No = 80 (91.96%) | 0.01 |
Nulliparous (n/%) | Yes = 49 (56.32%) No = 38 (43.68%) | Yes = 32 (36.78%) No = 55 (63.22%) | 0.009 |
BMI, kg/m2, (mean and standard deviation) | 30.11 ± 2.92 | 23.17 ± 2.78 | 0.04 |
Smoking (n/%) | Yes = 3 (3.44%) No = 84 (96.56%) | Yes = 7 (8.04%) No = 80 (91.96%) | 0.19 |
Diabetes (n/%) | Yes = 14 (16.09%) No = 73 (83.9%) | Yes = 4 (6.6%) No = 83 (95.4%) | 0.01 |
Previous diagnosis of chronic hypertension (n/%) | Yes = 5 (5.74%) No = 82 (94.25%) | Yes = 2 (2.29%) No = 85 (97.7%) | 0.24 |
Previous diagnosis of autoimmune disorders (n/%) | Yes = 8 (9.19%) No = 79 (90.8%) | Yes = 3 (3.44%) No = 84 (96.56%) | 0.11 |
Previous diagnosis of adverse pregnancy outcomes (n/%) | Yes = 11 (12.64%) No = 76 (87.36%) | Yes = 3 (3.44%) No = 84 (96.56%) | 0.02 |
Adverse Pregnancy Outcomes | PCOS vs. Control Group | Magnitude of the Confounding | |||
---|---|---|---|---|---|
Crude Estimates | p Value | M-H Combined | p Value ** | ||
OR and 95% CI | aOR and 95% CI | ||||
Gestational diabetes * IVF | 5.51 (1.30–17.72) | 0.01 | 3.12 (0.47–20.58) | 0.23 | 0.77 |
Gestational diabetes * Obesity | 5.01 (0.41–59.65) | 0.19 | 0.10 | ||
Gestational diabetes * Diabetes | 2.5 (0.25–24.37) | 0.61 | 1.20 | ||
Gestational diabetes * Previous APO | 5 (0.21–117.89) | 0.30 | 0.10 | ||
Fetal macrosomia * IVF | 3.71 (0.84–9.03) | 0.08 | 1.59 (0.23–10.57) | 0.63 | 1.33 |
Fetal macrosomia * Obesity | 1.4 (0.14–13.56) | 0.77 | 1.65 | ||
Fetal macrosomia * Diabetes | 1 (0.10–9.22) | 0.99 | 2.71 | ||
Fetal macrosomia * APO | 0.87 (0.05–12.97) | 0.92 | 3.26 | ||
Preterm birth * IVF | 6.15 (1.27–58.33) | 0.009 | 3.92.(0.59–26.10 | 0.15 | 0.57 |
Preterm birth * Obesity | 11 (0.64–187.16) | 0.07 | −0.44 | ||
Preterm birth * Diabetes | 3.66 (0.35–38.02) | 0.27 | 0.68 | ||
Preterm birth * APO | 5.5(0.23–128.96) | 0.27 | 0.12 | ||
PE * IVF | 1.51 (0.16–18.55) | 0.49 | 0.5 (0.06–3.90) | 0.51 | 2.02 |
PE * Obesity | 0.33 (0.03–3.51) | 0.36 | 3.58 | ||
PE * Diabetes | 0.27 (0.02–2.82) | 0.27 | 4.59 | ||
PE * APO | 0.16 (0.01–2.56) | 0.18 | 8.44 | ||
Gestational hypertension * IVF | 1.70 (0.31–11.31) | 0.29 | 0.51 (0.08–3.15) | 0.47 | 2.33 |
Gestational hypertension * Obesity | 0.23 (0.01–3.01) | 0.26 | 6.39 | ||
Gestational hypertension * Diabetes | 0.18 (0.01–2.28) | 0.17 | 8.44 | ||
Gestational hypertension * APO | 0.71 (0.22–2.25) | 0.56 | 1.39 | ||
IUGR * IVF | 1.34 (0.22–9.47) | 0.69 | 0.38 (0.05–2.45) | 0.31 | 2.53 |
IUGR * Obesity | 0.16 (0.01–2.15) | 0.15 | 7.38 | ||
IUGR * Diabetes | 0.13 (0.01–1.69) | 0.10 | 3.47 | ||
IUGR * APO | 0.5 (0.15–1.66) | 0.25 | 1.68 | ||
Cesarean delivery * IVF | 2.53 (1.29–4.99) | 0.003 | 2.15 (1.11–4.15) | 0.02 | 0.18 |
Cesarean delivery * Obesity | 2.25 (1.18–4.30) | 0.01 | 0.12 | ||
Cesarean delivery * Diabetes | 2.16 (1.13–4.13) | 0.01 | 0.17 | ||
Cesarean delivery * APO | 2.2 (1.15–4.18) | 0.01 | 0.15 | ||
NICU admission * IVF | 2.37 (0.78–7.97) | 0.08 | 0.43 (0.04–4.56) | 0.48 | 4.51 |
NICU admission * Obesity | 0.54 (0.04–6.16) | 0.62 | 3.39 | ||
NICU admission * Diabetes | 0.63 (0.24–1.64) | 0.35 | 2.76 | ||
NICU admission * APO | 0.35 (0.02–5.10) | 0.56 | 5.77 | ||
Need for invasive ventilation * IVF | 1.70 (0.31–11.31) | 0.46 | 0.48–13.73 | 0.26 | 2.54 |
Need for invasive ventilation * Obesity | 0.23 (0.01–3.01) | 0.26 | 6.39 | ||
Need for invasive ventilation * Diabetes | 0.18 (0.01–2.28) | 0.17 | 8.44 | ||
Need for invasive ventilation * APO | 1.14 (0.41–3.15) | 0.79 | 0.49 | ||
Necrotizing enterocolitis * IVF | 1.51 (0.16–18.55) | 0.65 | 0.5 (0.06–3.90) | 0.51 | 2.02 |
Necrotizing enterocolitis * Obesity | 0.33 (0.03–3.51) | 0.36 | 3.58 | ||
Necrotizing enterocolitis * Diabetes | 0.27 (0.02–2.82) | 0.27 | 4.59 | ||
Necrotizing enterocolitis * APO | 0.16 (0.01–2.56) | 0.18 | 8.44 | ||
Neonatal death * IVF | 2.02 (0.10–120.73) | 0.56 | 0.75 (0.05–9.87) | 0.82 | 1.69 |
Neonatal death * Obesity | 0.6 (0.03–9.15) | 0.72 | 2.37 | ||
Neonatal death * Diabetes | 0.5 (0.03–7.54) | 0.62 | 3.04 | ||
Neonatal death * APO | 0.4 (0.02–6.84) | 0.53 | 4.05 |
Clinical Predictors | Gestational Diabetes | Fetal Macrosomia | Preterm Birth | ||||||
---|---|---|---|---|---|---|---|---|---|
RR | 95%CI | p Value | RR | 95%CI | p Value | RR | 95%CI | p Value | |
Age > 35 years | 0.76 | −0.24–8.56 | 0.42 | 0.42 | 0.03–4.98 | 0.56 | 2.19 | 0.48–12.31 | 0.06 |
IVF conception | 3.18 | 0.98–18.22 | 0.01 | 4.63 | 1.22–17.39 | 0.03 | 5.24 | 1.74–21.73 | 0.02 |
Nulliparity | 0.44 | −0.12–9.03 | 0.59 | 1.04 | 0.78–7.78 | 0.32 | 0.33 | −0.71–8.64 | 0.86 |
Obesity | 5.31 | 1.16–32.88 | 0.02 | 3.46 | 1.19–14.83 | 0.04 | 2.37 | −1.16–11.2 | 0.32 |
Smoking status | 0.56 | −0.76–5.52 | 0.17 | −0.11 | −2.31–2.89 | 0.46 | 1.12 | 0.04–3.85 | 0.05 |
Diabetes | 5.28 | 0.26–14.95 | 0.001 | 6.93 | 1.32–13.79 | 0.001 | 3.14 | 0.64–9.25 | 0.03 |
History of adverse pregnancy outcomes | 1.24 | 0.02–4.86 | 0.78 | 1.89 | 0.71–7.98 | 0.67 | 3.97 | 0.14–10.22 | 0.02 |
Models | Gestational Diabetes | Fetal Macrosomia | Preterm Birth | ||||||
---|---|---|---|---|---|---|---|---|---|
Sensibility | Specificity | AUC Value | Sensibility | Specificity | AUC Value | Sensibility | Specificity | AUC Value | |
DT | 44.44 | 87.50 | 0.694 | 44.44 | 80.58 | 0.625 | 55.56 | 87.50 | 0.713 |
NB | 55.56 | 88.46 | 0.731 | 55.56 | 88.46 | 0.754 | 66.67 | 88.46 | 0.778 |
SVM | 66.67 | 61.17 | 0.693 | 66.67 | 80.58 | 0.721 | 77.78 | 87.38 | 0.883 |
RF | 88.12 | 78.16 | 0.782 | 88.91 | 81.26 | 0.897 | 89.77 | 86.23 | 0.901 |
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Mogos, R.; Gheorghe, L.; Carauleanu, A.; Vasilache, I.-A.; Munteanu, I.-V.; Mogos, S.; Solomon-Condriuc, I.; Baean, L.-M.; Socolov, D.; Adam, A.-M.; et al. Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms. Medicina 2024, 60, 1298. https://doi.org/10.3390/medicina60081298
Mogos R, Gheorghe L, Carauleanu A, Vasilache I-A, Munteanu I-V, Mogos S, Solomon-Condriuc I, Baean L-M, Socolov D, Adam A-M, et al. Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms. Medicina. 2024; 60(8):1298. https://doi.org/10.3390/medicina60081298
Chicago/Turabian StyleMogos, Raluca, Liliana Gheorghe, Alexandru Carauleanu, Ingrid-Andrada Vasilache, Iulian-Valentin Munteanu, Simona Mogos, Iustina Solomon-Condriuc, Luiza-Maria Baean, Demetra Socolov, Ana-Maria Adam, and et al. 2024. "Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms" Medicina 60, no. 8: 1298. https://doi.org/10.3390/medicina60081298