A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth

A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth

© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Dr. R. Surendiran, R. Aarthi, M. Thangamani, S. Sugavanam, R. Sarumathy
DOI :  10.14445/22315381/IJETT-V70I5P207

How to Cite?

Dr. R. Surendiran, R. Aarthi, M. Thangamani, S. Sugavanam, R. Sarumathy, "A Systematic Review using Machine Learning Algorithms for Predicting Preterm Birth," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 46-59, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P207

Preterm births (PTB) affect nearly 15 million kids worldwide. At present, medical fields aim to reduce the possessions of prematurity rather than avoid it. The cervix is currently measured during a transvaginal ultrasound, used to diagnose the condition. Because of the complexities of this process, preterm births cannot be accurately predicted. Machine learning is becoming more popular for prediction and diagnosis in health care. This study looks at how artificial intelligence can predict preterm labor and birth. According to this study, various machine learning approaches can aid in the diagnosis of preterm births. In terms of predicting preterm birth, machine learning can be well suited for various data types. Electro hysterogram signals, electronic health records, and transvaginal ultrasounds are examples. This review`s goal remains to summarize machine learning procedures intended for predicting premature birth.

Prediction, Preterm birth, Artificial Intelligence, Machine learning.

[1] World Health Organization. Preterm birth. (2015), URL http://www.who.int/ media centre/factsheets/fs363/en/.
[2] Cost effects of Preterm birth: a Comparison of healthcare Costs associated with early preterm, late preterm, and full-term birth in the first 3years after birth J Jacob, MLehne, A Mischker, N Klinger. – The European Journal (2017)-Springer.
[3] Behrman, R. E., & Butler, A. S. Preterm birth: Causes, Consequences, and Prevention. (2007).
[4] Hintz, S. R., Newman, J. E., &Vohra, B. R. Changing Definitions of Long-term follow-up: should “long term” be even longer? In Seminars in perinatology . WB Saunders. 40 (6) (2016) 398-409.
[5] World Health Organisation. (1977). WHO: Recommended Definitions, Terminology, and Format for Statistical Tables related to the Perinatal Period and use of a new Certificate for cause of Perinatal deaths? Modifications Recommended by FIGO as amended Acta Obstet Gynecol Scand, 56(3), (1976) 247-53.
[6] Radford, S. K., Costa, F. D. S., Júnior, E. A., & Sheehan, P. M. Clinical Application of Quantitative Fetal Fibronectin for Predicting Preterm birth in Symptomatic Women. Gynecologic and Obstetric Investigation, 83(3) (2018) 285-289.
[7] Blencowe, H., Cousens, S., Chou, D., Oestergaard, M., Say, L., Moller, A. B., & Lawn,J. Born too soon: the Global Epidemiology of 15 Million Preterm births. Reproductive Health, 10(1) (2013) 1-14.
[8] Berghella, V., Palacio, M., Ness, A., Alfirevic, Z., Nicolaides, K. H., &Saccone, G. Cervical Length Screening for Prevention of Preterm birth in a Singleton Pregnancy with Threatened Preterm Labor: Systematic Review and Meta?Analysis of Randomized Controlled Trials using Individual Patient?level data. Ultrasound in Obstetrics & Gynecology, 49(3) (2017) 322-329.
[9] Mercer BM, Goldenberg RL, Das A, Moawad AH, Iams JD, Meis PJ, et al. The preterm prediction study: a clinical risk assessment system. Am J Obstet Gynecol 1996 Jun;174(6):1885-1895?
[10] Surendiran,R., and Duraisamy, K., An Approach in Semantic Web Information Retrieval. IJETT International Journal of Electronics and Communication Engineering,1(1), ISSN:2348-8549, (2014) 17-21. https://doi.org/10.14445/23488549/IJECE-V1I1P105
[11] Shahid N, Rappon T, Berta W. Applications of Artificial Neural Networks in Healthcare Organizational Decision-Making: a scoping review. PLoS One (2019).
[12] Fatima M, Pasha M. Survey of Machine Learning Algorithms for Disease Diagnostic. J Intell Learn SystAppl 09 (2017) 1–16.
[13] Koivu A, Sairanen M. Predicting Risk of Stillbirth and Preterm Pregnancies with Machine Learning. Health Inf Sci Syst 8(1) (2020) 14.
[14] Safi Z, Venugopal N, Ali H, Makhlouf M, Boughorbel S. Analysis of Risk Factors Progression of Preterm Delivery using Electronic Healthrecords. (2020).
[15] Sufriyana H, Husnayain A, Chen YL, Kuo CY, Singh O, Yeh TY, et al. Comparison of Multivariable logistic Regression and Other Machine Learning Algorithms for Predictive Prediction Studies in Pregnancy care: Systematic Review and Meta-analysis. JMIR Med Inform; e16503. 8(11) (2020) 17.
[16] W?odarczyk, T.; P?otka, S.; Trzci ´nski, T.; Rokita, P.; Sochacki-Wójcicka, N.; Lipa, M.; Wójcicki, J. Estimation of Preterm Birth Markers with U-Net Segmentation Network. In Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis; Wang, Q., Gomez, A., Hutter, J., McLeod, K., Zimmer, V., Zettinig, O., Licandro, R., Robinson, E., Christiaens, D., Turk, E.A., et al., Eds.; Springer: Cham, Switzerland, (2019) 95103.
[17] S. Saigal and L. W. Doyle, “An Overview of Mortality and Sequelae of Preterm birth From infancy to adulthood,” 8e Lancet, 371 (9608) (2008) 261–269,
[18] R. Pari, M. Sandhya, and S. Sankar, “Risk Factors-Based Classification for Accurate Prediction of the Preterm Birth,” in Proceedings of the 2017 International Conference on Inventive Computing and Informatics (ICICI) IEEE, Coimbatore, India. (2017) 394–399.
[19] Wainberg, M., Merico, D., Delong, A. et al. Deep Learning in Biomedicine. Nat Biotechnol 36(2018) 829–838. https://doi.org/10.1038/nbt.4233
[20] Ngiam K. Y., Khor I. W. Big Data and Machine Learning Algorithms for Healthcare Delivery. The Lancet Oncology. 20(5) (2019)e262–e273. doi: 10.1016/s1470- 2045(19)30149 4.
[21] Rajkumar A., Dean J., Kohane I. Machine learning in Medicine. New England Journal of Medicine. 380(14) (2019)1347–1358. doi: 10.1056/nejmra1814259
[22] Witten I. H., Frank E. Data mining. ACM Sigmod Record. 31(1) (2002) 76–77. doi: 10.1145/507338.507355.
[23] Dhillon A., Singh A. Machine Learning in Healthcare Data Analysis: a Survey. Journal of Biology and Today’s World. 8(6) (2019)1–10.
[24] Ahmad M. A., Eckert C., Teredesai A. Interpretable Machine Learning in Healthcare. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics; Washington, DC, USA. (2018)559–560.
[25] Surendiran,R., Thangamani,M., Narmatha,C., Iswarya,M., Effective Autism Spectrum Disorder Prediction to Improve the Clinical Traits using Machine Learning Techniques, International Journal of Engineering Trends and Technology (IJETT),., ISSN: 2231- 5381, 70(4) (2022) 343-359. https://doi.org/10.14445/22315381/IJETT-V70I4P230
[26] Kotsiantis, S.B. Decision trees: a recent overview. Artif. Intell. Rev. 39 (2013) 261–283
[27] Breiman, L. Random forests. Mach. Learn. 45 (2001) 5–32
[28] Chen, T., and Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD SIGKDD. 16 (2016) 785–794.
[29] Hsu, C.-W. and Lin, C.-J. A Comparison of Methods for Multiclass Support Vector Machines. IEEE Trans. Neural Netw. 13 (2002) 415–425
[30] Altman, N.S. An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. Am. Stat. 46 (1992) 175–185.
[31] Friedman, N. et al. Bayesian network classifiers. Mach. Learn. 29 (1997) 131–163.
[32] Miotto, R. et al. Deep Learning for Healthcare: Review, Opportunities, and Challenges. Brief. Bioinform. 19 (2018) 1236–1246
[33] Eraslan, G. et al. Deep Learning: new Computational Modeling Techniques for Genomics. Nat. Rev. Genet. 20 (2019) 389–403
[34] Cheerla, A. and Gevaert, O. Deep learning with Multimodal Representation for Pan-Cancer Prognosis Prediction. Bioinformatics 35 (2019) 446–454
[35] Nieto-del-Amor, F.; Prats-Boluda, G.; Martinez-De-Juan, J.L.; Diaz-Martinez, A.; Monfort-Ortiz, R.; Diago-Almela, V.J.; Ye-Lin, Y. Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction based on Electrogastrography. Sensors 21 (2021) 3350.
[36] Peng, J.; Hao, D.; Yang, L.; Dua, M.; Song, X.; Jiang, H.; Zhang, Y.; Zheng, D. Evaluation of Electrogastrogram Measured from Different Gestational Weeks for Recognizing Preterm Delivery: A Preliminary Study Using Random Forest. Biocybern. Biomed. Eng. 40 (2020) 352–362.
[37] Fergus, P.; Idowu, I.; Hussain, A.; Dobbins, C. Advanced Artificial Neural Network Classification for Detecting Preterm Births using EHG records. Neurocomputing 188 (2016) 42–49.
[38] Fergus, P.; Cheung, P.; Hussain, A.; Al-Jumeily, D.; Dobbins, C.; Iram, S. Prediction of Preterm Deliveries from EHG Signals using Machine Learning. PLoS ONE 8 (2013)e77154.
[39] Despotovic, D.; Zec, A.; Mladenovic, K.; Radin, N.; Turukalo, T.L. A Machine Learning Approach for an Early Prediction of Preterm Delivery. In Proceedings of the 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia, 13 (15) (2018) 265–270.
[40] Acharya, U.R.; Sudarshan, VK; Rong, S.Q.; Tan, Z.; Min, L.C.; Koh, J.E.; Nayak, S.; Bhandary, S.V. Automated Detection of Premature Delivery using Empirical Model and Wavelet Packet Decomposition Techniques with Uterine Electromyogram Signals. Comput. Biol. Med. 85 (2017) 33–42.
[41] Khan, M.U.; Sajid, Z.; Sohail, M.; Aziz, S.; Ibraheem, S.; Naavi, S.Z.H. Electrohysterogram based Term, and Preterm Delivery Classification System. In Proceedings of the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia, 3(5) (2020) 83–88.
[42] Degbedzui, D.K.; Yüksel, M.E. Accurate Diagnosis of the Term–Preterm births by Spectral Analysis of Electrohysterography signals. Comput. Biol. Med. 119 (2020) 103677.
[43] Ren, P.; Yao, S.; Li, J.; Valdes-Sosa, P.A.; Kendrick, K.M. Improved Prediction of Preterm Delivery using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.PLoS ONE, 10 (2015) e0132116.
[44] Sadi-Ahmed, N.; Kacha, B.; Taleb, H.; Kedir-Talha, M. Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy Electro Hysterograhic (EHG) records. J. Med. Syst. 41 (2017) 1–13.
[45] Hangxiao Lou, Haifeng Liu, Zhenqin Chen, Zi’ang Zhen, Bo Dong, Jinshan Xu, Bioprocess Inspired Characterization of Pregnancy Evolution using Entropy and its Application in Preterm birth Detection, Biomedical Signal Processing, and Control, Volume 75(2022)103587, ISSN 1746-8094.
[46] M. Shahbakhti, M. Beiramvand, M. R. Bavi, and S. Mohammadi Far, A New Efficient Algorithm for Prediction of Preterm Labor, (2019) 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2019) 4669- 4672, doi: 10.1109/EMBC.2019.8857837.
[47] Mohammadi Far S, Beiramvand M, Shahbakhti M, Augustyniak P. Prediction of Preterm Delivery from Unbalanced EHG Database. Sensors. 22(4) (2022) 1507. https://doi.org/10.3390/s22041507
[48] Prats-Boluda G, Pastor-Tronch J, Garcia-Casado J, Monfort-Ortíz R, Perales Marín A, Diago V, Roca Prats A, Ye-Lin Y. Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography. Sensors. 21(7) (2021) 2496. https://doi.org/10.3390/s21072496.
[49] Zhang, L., Li, H., Li, J. et al. Prediction of Iatrogenic Preterm birth in Patients with Scarred Uterus: a Retrospective Cohort Study in Northeast China. BMC Pregnancy Childbirth 20 (2020) 490 https://doi.org/10.1186/s12884-020-03165-7
[50] Shaniba Asmi P., Kamalraj Subramaniam, Nisheena V. Iqbal, A review of significant Research on Predicting Preterm Birth using Uterine Electromyogram Signal, Future Generation Computer Systems, ISSN 0167-739X, 98(2019)135-143 https://doi.org/10.1016/j.future.2018.10.033.
[51] Vovsha, I.; Rajan, A.; Salleb-Aouissi, A.; Raja, A.; Radeva, A.; Diab, H.; Tomar, A.; Wapner, R. Predicting Preterm Birth is not Elusive: Machine Learning Paves the way to Individual Wellness. In Proceedings of the 2014 AAAI Spring Symposium Series, Palo Alto, CA, USA, 24 (26)(2014) 82–89.
[52] Tran, T.; Luo, W.; Phung, D.; Morris, J.; Rickard, K.; Venkatesh, S. Preterm birth Prediction: Stable Selection of Interpretable Rules from High Dimensional data. In Proceedings of the 1st Machine Learning for Healthcare Conference, Los Angeles, CA, USA, 19(20) (2016)164–177.
[53] Esty, A.; Frize, M.; Gilchrist, J.; Bariciak, E. Applying Data Preprocessing Methods to Predict Premature Birth. In Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18 (21)(2018) 6096–6099.
[54] Weber A, Darmstadt GL, Gruber S, Foeller ME, Carmichael SL, Stevenson DK, et al. Application of Machine-Learning to Predict early Spontaneous Preterm birth Among Nulliparous non-Hispanic Black and White Women. Ann Epidemiol 28(11) (2018) 783- 9.e1. [doi: 10.1016/j.annepidem.2018.08.008] [Medline: 30236415]
[55] M. W. L. Moreira, J. J. P. C. Rodrigues, G. A. B. Marcondes, A. J. V. Neto, N. Kumar AndI. De La Torre Diez, A Preterm Birth Risk Prediction System for Mobile Health Applications Based on the Support Vector Machine Algorithm, 2018 IEEE International Conference on Communications (ICC), (2018)1-5, doi: 10.1109/ICC.2018.8422616.
[56] Prema, NS; Pushpalatha, M.P. Machine Learning Approach for Preterm Birth Prediction Based on Maternal Chronic Conditions. In Lecture Notes in Electrical Engineering; Sridhar, V., Padma, M., Rao, K., Eds; Springer: Singapore, (2019) 581–588.
[57] Lee, K.S.; Ahn, KH Artificial Neural Network Analysis of Spontaneous Preterm Labor and Birth and Its Major Determinants. J. Korean Med. Sci. 34 (2019).
[58] Gao C, Osmundson S, Velez Edwards DR, Jackson GP, Malin BA, Chen Y. Deep Learning Predicts Extreme Preterm Birth from Electronic Health Records. J Biomed Inform. 100:103334. doi: 10.1016/j.jbi.2019.103334. Epub 2019 Oct 31. PMID: 31678588; PMCID: PMC6899197. (2019)
[59] Puspitasari, Diah, Ramanda, Kresna, Supriyatna, Adi, Wahyudi, Mochamad, Delima, Sikumbang, Erma, Hadisukmana, sulaeman, Comparison of Data Mining Algorithms UsingArtificial Neural Networks (ANN) and Naive Bayes for Preterm Birth Prediction, Journal of Physics: Conference Series, Volume 1641, International Conference on Advanced Information Scientific Development (ICAISD) (2020)
[60] Arabi Belaghi, R.Beyene, J.&McDonald, S.D.Clinical Risk Models for Preterm birth less than 28 weeks and less than 32 weeks of Gestation using a Large Retrospective Cohort. J Perinatol 41 (2021) 2173-2181.
[61] Hasan Rawashdeh, ShathaAwawdeh, Fatima Shannag, EsraaHenawi, Hossam Faris, Nadim Obeid, Jon Hyett, Intelligent System Based on Data Mining Techniques for Prediction of Preterm Birth for Women with Cervical Cerclage, Computational Biology, and Chemistry, 85(2020)107233 ISSN 1476-9271, https://doi.org/10.1016/j.compbiolchem.2020.107233.
[62] Enio Luis Damaso, Daniel Lober Rolnik, Ricardo de Carvalho Cavalli, Silvana Maria Quintana, Geraldo Duarte, Fabricio da Silva Costa, Alessandra Marcolin, Prediction of Preterm Birth by Maternal Characteristics and Medical History in the Brazilian Population, Journal of Pregnancy, Article ID 4395217, 6 pages, (2019). https://doi.org/10.1155/2019/4395217
[63] Boughorbel, S., Jarray, F., Venugopal, N., &Elhadi, H. Alternating loss correction for preterm-birth prediction from ehr data with noisy labels. arXiv preprint arXiv:1811.09782. (2018).
[64] Barbara Lizewska, Joanna Teul, Pawel Kuc, Adam Lemancewicz, Karol Charkiewicz, Joanna Goscik, Marian Kacerovsky, Ramkumar Menon, Wojciech Miltyk, Piotr Laudanski, "Maternal Plasma Metabolomic Profiles in Spontaneous Preterm Birth: Preliminary Results, Mediators of Inflammation, (2018), Article ID 9362820, 13 pages, (2018) https://doi.org/10.1155/2018/9362820
[65] Díaz, E., Fernández-Plaza, C., Abad, I. et al. Machine Learning as a Tool to Study the Influence of Chronodisruption in Preterm Births. J Ambient Intell Human Comput 13(2022) 381–392 https://doi.org/10.1007/s12652-021-02906-6
[66] W?odarczyk, T.; P?otka, S.; Rokita, P.; Sochacki-Wójcicka, N.; Wójcicki, J.; Lipa, M.; Trzci ´nski, T. Spontaneous Preterm Birth Prediction Using Convolutional Neural Networks. In Medical Ultrasound, and Preterm, Perinatal, and Paediatric Image Analysis; Hu, Y., Licandro, R., Noble, J.A., Hutter, J., Aylward, S., Melbourne, A., Turk, E.A., Barrena, JT, Eds.; Springer: Cham, Switzerland, (2020)274–283.
[67] Rawashdeh, H.; Awawdeh, S.; Shannag, F.; Henawi, E.; Faris, H.; Obeid, N.; Hyett, J. Intelligent System Based on Data Mining Techniques for Prediction of Preterm Birth for Women with Cervical Cerclage. Comput. Biol. Chem. 85 (2020) 107233.
[68] Grigorescu, I.; Cordero-Grande, L.; Edwards, A.D.; Hajnal, J.; Modat, M.; Deprez, M. Interpretable convolutional neural Networks for Preterm Birth Classification. arXiv, arXiv:1910.00071. (2019)
[69] T.A.H. Rocha, EBAF de Thomaz, DG de Almeida, et al. Data-driven Risk Stratification for Preterm Birth in Brazil: A Population-Based Study to Develop of a Machine Learning Risk Assessment Approach, The Lancet Regional Health - Americas 3 (2021) 100053.
[70] Baer RJ, McLemore MR, Adler N, Oltman SP, Chambers BD, Kuppermann M, Pantell MS, Rogers EE, Ryckman KK, Sirota M, Rand L, Jelliffe-Pawlowski LL. Pre-Pregnancy or First-Trimester Risk Scoring to Identify Women at High Risk of Preterm Birth. Eur J Obstet Gynecol Reprod Biol. 231(2018) 23540.
[71] Zou, H. and Hastie, T. Regularization and variable selection via the elastic net. JR Stat. Soc. Ser. B Stat Methodol. 67 (2005)301–320.