{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T21:10:59Z","timestamp":1767474659259,"version":"3.48.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T00:00:00Z","timestamp":1767398400000},"content-version":"vor","delay-in-days":14,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100006013","name":"United Arab Emirates University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-29219-4","type":"journal-article","created":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T08:32:59Z","timestamp":1766219579000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Early prediction of very and extreme preterm births using a one-class classification framework on electronic health records in UAE"],"prefix":"10.1038","volume":"16","author":[{"given":"Amir","family":"Ahmad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wasif","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Mozakkir","family":"Ansari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad M.","family":"Masud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadirah","family":"Ghenimi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luai A.","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,20]]},"reference":[{"key":"29219_CR1","unstructured":"WHO | World Health Organization. Accessed: Dec. 21, 2020. [Online]. Available: https:\/\/www.who.int\/"},{"key":"29219_CR2","doi-asserted-by":"publisher","unstructured":"Goldenberg, R. L., Culhane, J. F., Iams, J. D. & Romero, R. Epidemiology and causes of preterm birth. Lancet 371 (9606), 75\u201384. https:\/\/doi.org\/10.1016\/S0140-6736(08)60074-4 (Jan. 2008).","DOI":"10.1016\/S0140-6736(08)60074-4"},{"key":"29219_CR3","doi-asserted-by":"publisher","unstructured":"Howson, C. P., Kinney, M. V., McDougall, L. & Lawn, J. E. Born Toon Soon: Preterm birth matters, Reproductive Health, vol. 10, no. SUPPL. 1, Art. no. SUPPL. 1, Nov. (2013). https:\/\/doi.org\/10.1186\/1742-4755-10-S1-S1\/FIGURES\/6","DOI":"10.1186\/1742-4755-10-S1-S1\/FIGURES\/6"},{"key":"29219_CR4","doi-asserted-by":"publisher","unstructured":"Moutquin, J. M. Classification and heterogeneity of preterm birth, BJOG: An International Journal of Obstetrics & Gynaecology, vol. 110, no. s20, pp. 30\u201333, (2003). https:\/\/doi.org\/10.1046\/j.1471-0528.2003.00021.x","DOI":"10.1046\/j.1471-0528.2003.00021.x"},{"key":"29219_CR5","doi-asserted-by":"publisher","unstructured":"Barfield, W. D. Public health implications of very preterm birth. Clin. Perinatol. 45 (3), 565\u2013577. https:\/\/doi.org\/10.1016\/j.clp.2018.05.007 (Sep. 2018).","DOI":"10.1016\/j.clp.2018.05.007"},{"key":"29219_CR6","doi-asserted-by":"publisher","unstructured":"Acharya, U. R. et al. Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Comput. Biol. Med. 85, 33\u201342. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.04.013 (Jun. 2017).","DOI":"10.1016\/j.compbiomed.2017.04.013"},{"key":"29219_CR7","doi-asserted-by":"publisher","unstructured":"Horoba, K. et al. Automated detection of uterine contractions in tocography signals \u2013 Comparison of algorithms, Biocybernetics and Biomedical Engineering, vol. 36, no. 4, Art. no. 4, Jan. (2016). https:\/\/doi.org\/10.1016\/j.bbe.2016.08.005","DOI":"10.1016\/j.bbe.2016.08.005"},{"key":"29219_CR8","unstructured":"Monitoring uterine activity during labor: a comparison of 3 methods - American Journal of Obstetrics & Gynecology. Accessed: Jul. 31, 2023. [Online]. Available: https:\/\/www.ajog.org\/article\/S0002-9378(12)01973-4\/fulltext"},{"key":"29219_CR9","doi-asserted-by":"publisher","unstructured":"Sun, Q. et al. Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record, Journal of Healthcare Engineering, vol. pp. 1\u201312, Apr. 2022, (2022). https:\/\/doi.org\/10.1155\/2022\/9635526","DOI":"10.1155\/2022\/9635526"},{"key":"29219_CR10","doi-asserted-by":"publisher","unstructured":"Lauritsen, S. M. et al. Jul., Explainable artificial intelligence model to predict acute critical illness from electronic health records, Nat Commun, vol. 11, no. 1, Art. no. 1, (2020). https:\/\/doi.org\/10.1038\/s41467-020-17431-x","DOI":"10.1038\/s41467-020-17431-x"},{"key":"29219_CR11","doi-asserted-by":"publisher","unstructured":"Bean, D. M. et al. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Sci. Rep. 7 (1), 16416. https:\/\/doi.org\/10.1038\/s41598-017-16674-x (Dec. 2017).","DOI":"10.1038\/s41598-017-16674-x"},{"key":"29219_CR12","doi-asserted-by":"publisher","unstructured":"Despotovi\u0107, D., Zec, A., Mladenovic, K., Radin, N. & Loncar-Turukalo, T. A machine learning approach for an early prediction of preterm delivery. Sep 000265\u2013000270. https:\/\/doi.org\/10.1109\/SISY.2018.8524818 (2018).","DOI":"10.1109\/SISY.2018.8524818"},{"key":"29219_CR13","doi-asserted-by":"publisher","unstructured":"Degbedzui, D. K. & Y\u00fcksel, M. E. Accurate diagnosis of term-preterm births by spectral analysis of electrohysterography signals. Comput. Biol. Med. 119, 103677. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103677 (Apr. 2020).","DOI":"10.1016\/j.compbiomed.2020.103677"},{"key":"29219_CR14","doi-asserted-by":"publisher","unstructured":"Xu, J. et al. Realistic preterm prediction based on optimized synthetic sampling of EHG signal. Comput. Biol. Med. 136, 104644. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104644 (Sep. 2021).","DOI":"10.1016\/j.compbiomed.2021.104644"},{"key":"29219_CR15","doi-asserted-by":"publisher","unstructured":"Rawashdeh, H. et al. Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage. Comput. Biol. Chem. 85, 107233. https:\/\/doi.org\/10.1016\/j.compbiolchem.2020.107233 (Apr. 2020).","DOI":"10.1016\/j.compbiolchem.2020.107233"},{"key":"29219_CR16","doi-asserted-by":"publisher","unstructured":"Lou, H. et al. Bio-process inspired characterization of pregnancy evolution using entropy and its application in preterm birth detection. Biomed. Signal Process. Control. 75, 103587. https:\/\/doi.org\/10.1016\/j.bspc.2022.103587 (May 2022).","DOI":"10.1016\/j.bspc.2022.103587"},{"key":"29219_CR17","doi-asserted-by":"publisher","unstructured":"Vandewiele, G. et al. Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling. Artif. Intell. Med. 111, 101987. https:\/\/doi.org\/10.1016\/j.artmed.2020.101987 (Jan. 2021).","DOI":"10.1016\/j.artmed.2020.101987"},{"key":"29219_CR18","doi-asserted-by":"publisher","unstructured":"Esty, A., Frize, M., Gilchrist, J. & Bariciak, E. Applying Data Preprocessing Methods to Predict Premature Birth, Annu Int Conf IEEE Eng Med Biol Soc, vol. pp. 6096\u20136099, Jul. 2018, (2018). https:\/\/doi.org\/10.1109\/EMBC.2018.8513681","DOI":"10.1109\/EMBC.2018.8513681"},{"key":"29219_CR19","doi-asserted-by":"publisher","unstructured":"Idowu, I. O. et al. Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care, in., IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, Oct. 2015, pp. 215\u2013220. (2015). https:\/\/doi.org\/10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.31","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.31"},{"issue":"7","key":"29219_CR20","doi-asserted-by":"publisher","first-page":"e0132116","DOI":"10.1371\/journal.pone.0132116","volume":"10","author":"P Ren","year":"2015","unstructured":"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 (7), e0132116. https:\/\/doi.org\/10.1371\/journal.pone.0132116 (2015).","journal-title":"PLoS One"},{"key":"29219_CR21","doi-asserted-by":"publisher","unstructured":"Nieto-del-Amor, F. et al. Jan., Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography, Sensors, vol. 21, no. 10, Art. no. 10, (2021). https:\/\/doi.org\/10.3390\/s21103350","DOI":"10.3390\/s21103350"},{"key":"29219_CR22","doi-asserted-by":"publisher","first-page":"31883","DOI":"10.1109\/ACCESS.2019.2903568","volume":"7","author":"A Ahmad","year":"2019","unstructured":"Ahmad, A. & Khan, S. S. Survey of State-of-the-Art mixed data clustering algorithms. IEEE Access. 7, 31883\u201331902. https:\/\/doi.org\/10.1109\/ACCESS.2019.2903568 (2019).","journal-title":"IEEE Access."},{"key":"29219_CR23","doi-asserted-by":"publisher","unstructured":"Johansson, S., Jern, M. & Johansson, J. Interactive quantification of categorical variables in mixed data sets, Proceedings of the International Conference on Information Visualisation, pp. 3\u201310, (2008). https:\/\/doi.org\/10.1109\/IV.2008.33","DOI":"10.1109\/IV.2008.33"},{"key":"29219_CR24","doi-asserted-by":"publisher","unstructured":"Belaghi, R. A., Beyene, J. & McDonald, S. D. Prediction of preterm birth in nulliparous women using logistic regression and machine learning, PLOS ONE, vol. 16, no. 6, Art. no. 6, Jun. (2021). https:\/\/doi.org\/10.1371\/JOURNAL.PONE.0252025","DOI":"10.1371\/JOURNAL.PONE.0252025"},{"key":"29219_CR25","doi-asserted-by":"crossref","unstructured":"Jr, D. W. H., Lemeshow, S. & Sturdivant, R. X. Applied Logistic Regression (Wiley, 2013).","DOI":"10.1002\/9781118445112.stat06902"},{"key":"29219_CR26","doi-asserted-by":"publisher","unstructured":"Campos Trujillo, O., Perez-Gonzalez, J. & Medina-Ba\u00f1uelos, V. Early Prediction of Weight at Birth Using Support Vector Regression, in IFMBE Proceedings, Springer, Oct. pp. 37\u201341. (2020). https:\/\/doi.org\/10.1007\/978-3-030-30648-9_5","DOI":"10.1007\/978-3-030-30648-9_5"},{"key":"29219_CR27","doi-asserted-by":"publisher","unstructured":"Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J. & Scholkopf, B. Support vector machines, IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18\u201328, Jul. (1998). https:\/\/doi.org\/10.1109\/5254.708428","DOI":"10.1109\/5254.708428"},{"key":"29219_CR28","doi-asserted-by":"publisher","unstructured":"Breiman, L. Random Forests, Machine Learning, vol. 45, no. 1, pp. 5\u201332, Oct. (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","DOI":"10.1023\/A:1010933404324"},{"key":"29219_CR29","doi-asserted-by":"publisher","unstructured":"Gardner, M. W. & Dorling, S. R. Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences. Atmos. Environ. 32, 2627\u20132636. https:\/\/doi.org\/10.1016\/S1352-2310(97)00447-0 (Aug. 1998).","DOI":"10.1016\/S1352-2310(97)00447-0"},{"key":"29219_CR30","doi-asserted-by":"publisher","unstructured":"Sagi, O. & Rokach, L. Ensemble learning: A survey, WIREs Data Mining and Knowledge Discovery, vol. 8, no. 4, p. e1249, (2018). https:\/\/doi.org\/10.1002\/widm.1249","DOI":"10.1002\/widm.1249"},{"key":"29219_CR31","doi-asserted-by":"publisher","unstructured":"Mercer, B. M. et al. The preterm prediction study: A clinical risk assessment system. Am. J. Obstet. Gynecol. 174 (6), 1885\u20131895. https:\/\/doi.org\/10.1016\/S0002-9378(96)70225-9 (Jun. 1996).","DOI":"10.1016\/S0002-9378(96)70225-9"},{"key":"29219_CR32","doi-asserted-by":"publisher","unstructured":"Lee, K. S. & Ahn, K. H. Artificial neural network analysis of spontaneous preterm labor and birth and its major determinants. J. Korean Med. Sci. 34 (16). https:\/\/doi.org\/10.3346\/JKMS.2019.34.E128 (Apr. 2019).","DOI":"10.3346\/JKMS.2019.34.E128"},{"key":"29219_CR33","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1607.08310","author":"T Tran","year":"2016","unstructured":"Tran, T. et al. Preterm birth prediction: deriving stable and interpretable rules from high dimensional data. Jul https:\/\/doi.org\/10.48550\/arxiv.1607.08310 (2016).","journal-title":"Jul"},{"key":"29219_CR34","doi-asserted-by":"publisher","unstructured":"Taha, Z., Hassan, A. A., Wikkeling-Scott, L. & Papandreou, D. Factors associated with preterm birth and low birth weight in Abu Dhabi, the united Arab Emirates. Int. J. Environ. Res. Public Health. 17 (4). https:\/\/doi.org\/10.3390\/IJERPH17041382 (Feb. 2020).","DOI":"10.3390\/IJERPH17041382"},{"key":"29219_CR35","doi-asserted-by":"publisher","unstructured":"Koivu, A. & Sairanen, M. Predicting risk of stillbirth and preterm pregnancies with machine learning, Health Information Science and Systems, vol. 8, no. 1, Art. no. 1, Dec. (2020). https:\/\/doi.org\/10.1007\/s13755-020-00105-9","DOI":"10.1007\/s13755-020-00105-9"},{"key":"29219_CR36","doi-asserted-by":"publisher","unstructured":"Raja, R., Mukherjee, I. & Sarkar, B. K. A Machine Learning-Based Prediction Model for Preterm Birth in Rural India, Journal of Healthcare Engineering, vol. 2021, (2021). https:\/\/doi.org\/10.1155\/2021\/6665573","DOI":"10.1155\/2021\/6665573"},{"key":"29219_CR37","doi-asserted-by":"publisher","unstructured":"Al Haddad, A. et al. Mutaba\u2019ah\u2014Mother and child health study: protocol for a prospective cohort study investigating the maternal and early life determinants of infant, child, adolescent and maternal health in the united Arab Emirates. BMJ Open. 9 (8), e030937. https:\/\/doi.org\/10.1136\/bmjopen-2019-030937 (Aug. 2019).","DOI":"10.1136\/bmjopen-2019-030937"},{"key":"29219_CR38","doi-asserted-by":"publisher","unstructured":"Khan, W. et al. Jul., Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms, Sci Rep, vol. 12, no. 1, Art. no. 1, (2022). https:\/\/doi.org\/10.1038\/s41598-022-14393-6","DOI":"10.1038\/s41598-022-14393-6"},{"key":"29219_CR39","doi-asserted-by":"publisher","unstructured":"Khan, W. et al. Jan., Infant Low Birth Weight Prediction Using Graph Embedding Features, International Journal of Environmental Research and Public Health, vol. 20, no. 2, Art. no. 2, (2023). https:\/\/doi.org\/10.3390\/ijerph20021317","DOI":"10.3390\/ijerph20021317"},{"key":"29219_CR40","doi-asserted-by":"publisher","unstructured":"Weber, C. M., Ray, D., Valverde, A. A., Clark, J. A. & Sharma, K. S. Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1027, p. 166299, Mar. (2022). https:\/\/doi.org\/10.1016\/j.nima.2021.166299","DOI":"10.1016\/j.nima.2021.166299"},{"key":"29219_CR41","doi-asserted-by":"publisher","unstructured":"Uddin, S., Haque, I., Lu, H., Moni, M. A. & Gide, E. Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction, Sci Rep, vol. 12, no. 1, Art. no. 1, Apr. (2022). https:\/\/doi.org\/10.1038\/s41598-022-10358-x","DOI":"10.1038\/s41598-022-10358-x"},{"key":"29219_CR42","unstructured":"An In-Depth Study and Improvement of Isolation Forest. Accessed: Aug. 27, 2023. [Online]. Available: https:\/\/ieeexplore.ieee.org\/document\/9684896"},{"key":"29219_CR43","unstructured":"Bank, D., Koenigstein, N. & Giryes, R. Autoencoders. arXiv, Apr. 03, 2021. Accessed: Aug. 27, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/2003.05991"},{"key":"29219_CR44","doi-asserted-by":"publisher","unstructured":"Adaptive symbiotic organisms. Search (SOS) algorithm for structural design optimization. J. Comput. Des. Eng. 3 (3), 226\u2013249. https:\/\/doi.org\/10.1016\/j.jcde.2016.02.003 (Jul. 2016).","DOI":"10.1016\/j.jcde.2016.02.003"},{"key":"29219_CR45","unstructured":"Kamalov, F. & Elnagar, A. Kernel density estimation-based sampling for neural network classification. arXiv, Oct. 25, 2021. Accessed: Aug. 27, 2023. [Online]. Available: http:\/\/arxiv.org\/abs\/2110.12644"},{"key":"29219_CR46","doi-asserted-by":"publisher","unstructured":"Wang, S., Fan, Z., Li, Z., Zhang, H. & Wei, C. An effective lunar crater recognition algorithm based on convolutional neural network. Remote Sens. 12, https:\/\/doi.org\/10.3390\/rs12172694 (Jan. 2020). 17, Art. 17.","DOI":"10.3390\/rs12172694"},{"key":"29219_CR47","doi-asserted-by":"publisher","unstructured":"Yang, K., Kpotufe, S. & Feamster, N. An efficient One-Class SVM for anomaly detection in the internet of things, (2021). https:\/\/doi.org\/10.48550\/ARXIV.2104.11146","DOI":"10.48550\/ARXIV.2104.11146"},{"key":"29219_CR48","doi-asserted-by":"publisher","unstructured":"Das, R., Golatkar, A. & Awate, S. P. Sparse kernel PCA for outlier Detection. ArXiv. Sep 13 https:\/\/doi.org\/10.48550\/arXiv.1809.02497 (2018).","DOI":"10.48550\/arXiv.1809.02497"},{"key":"29219_CR49","doi-asserted-by":"publisher","unstructured":"Zhang, Z. & Deng, X. Anomaly detection using improved deep SVDD model with data structure preservation. Pattern Recognit. Lett. 148, 1\u20136. https:\/\/doi.org\/10.1016\/j.patrec.2021.04.020 (Aug. 2021).","DOI":"10.1016\/j.patrec.2021.04.020"},{"key":"29219_CR50","unstructured":"Pol, A. A., Berger, V., Cerminara, G., Germain, C. & Pierini, M. Anomaly Detection With Conditional Variational Autoencoders, arXiv.org. Accessed: Aug. 27, 2023. [Online]. Available: https:\/\/arxiv.org\/abs\/2010.05531v1"},{"key":"29219_CR51","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Lu, S., Wu, Y., Hu, W. & Yuan, Z. The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study, JMIR Medical Informatics, vol. 10, no. 6, Art. no. 6, Jun. (2022). https:\/\/doi.org\/10.2196\/33835","DOI":"10.2196\/33835"},{"key":"29219_CR52","doi-asserted-by":"publisher","unstructured":"Wolpert, D. H. Stacked generalization. Neural Netw. 5 (2), 241\u2013259. https:\/\/doi.org\/10.1016\/S0893-6080(05)80023-1 (Jan. 1992).","DOI":"10.1016\/S0893-6080(05)80023-1"},{"key":"29219_CR53","unstructured":"Zhao, Y., Nasrullah, Z. & Li, Z. PyOD: A Python Toolbox for Scalable Outlier Detection."},{"key":"29219_CR54","doi-asserted-by":"publisher","unstructured":"Davis, J. & Goadrich, M. The relationship between Precision-Recall and ROC curves, in Proceedings of the 23rd international conference on Machine learning - ICML \u201906, Pittsburgh, Pennsylvania: ACM Press, pp. 233\u2013240. (2006). https:\/\/doi.org\/10.1145\/1143844.1143874","DOI":"10.1145\/1143844.1143874"},{"issue":"9","key":"29219_CR55","doi-asserted-by":"publisher","first-page":"1796","DOI":"10.1109\/TKDE.2018.2806975","volume":"30","author":"SS Khan","year":"2018","unstructured":"Khan, S. S. Relationship between variants of one-class nearest neighbors and creating their accurate ensembles. IEEE Trans. Knowl. Data Eng. 30 (9), 1796\u20131809 (2018).","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"29219_CR56","volume-title":"Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science()","author":"SS Khan","year":"2010","unstructured":"Khan, S. S. & Madden, M. G. A survey of recent trends in one class classification. In: (eds Coyle, L. & Freyne, J.) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. (2010)."},{"key":"29219_CR57","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s40537-021-00514-x","volume":"8","author":"N Seliya","year":"2021","unstructured":"Seliya, N., Abdollah Zadeh, A. & Khoshgoftaar, T. M. A literature review on one-class classification and its potential applications in big data. J. Big Data. 8, 122 (2021).","journal-title":"J. Big Data"},{"key":"29219_CR58","doi-asserted-by":"publisher","first-page":"3490","DOI":"10.1016\/j.patcog.2013.05.022","volume":"46","author":"C Desir","year":"2013","unstructured":"Desir, C., Bernard, S., Petitjean, C. & Heutte, L. One class random forests. Pattern Recogn. 46, 3490\u20133506 (2013).","journal-title":"Pattern Recogn."},{"key":"29219_CR59","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.asoc.2017.01.034","volume":"55","author":"SS Khan","year":"2017","unstructured":"Khan, S. S., Karg, M. E., Kulia, D. & Hoey, J. Detecting falls with X-Factor hidden Markov models. Appl. Soft Comput. 55, 168\u2013177 (2017).","journal-title":"Appl. Soft Comput."},{"issue":"12","key":"29219_CR60","doi-asserted-by":"publisher","first-page":"e0293925","DOI":"10.1371\/journal.pone.0293925","volume":"18","author":"W Khan","year":"2023","unstructured":"Khan, W. et al. Predicting preterm birth using explainable machine learning in a prospective cohort of nulliparous and multiparous pregnant women. PLoS One. 18 (12), e0293925 (2023).","journal-title":"PLoS One"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29219-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29219-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29219-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T21:04:58Z","timestamp":1767474298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-29219-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,20]]},"references-count":60,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["29219"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-29219-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"type":"electronic","value":"2045-2322"}],"subject":[],"published":{"date-parts":[[2025,12,20]]},"assertion":[{"value":"18 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The study was conducted in accordance with the Declaration of Helsinki and approved by the United Arab Emirates University Human Research Ethics Committee (ERH-2017-5512) and the Abu Dhabi Health Research and Technology Ethics Committee (DOH\/CVDC\/2022\/72). Informed consent was obtained from all subjects involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}],"article-number":"78"}}