{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:02:58Z","timestamp":1764403378055,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T00:00:00Z","timestamp":1715904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at \u201cIESS Los Ceibos\u201d hospital in Guayaquil, Ecuador. Na\u00efve Bayes (NB), The Chow\u2013Liu Tree-Augmented Na\u00efve Bayes (TANcl), and Semi Na\u00efve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.<\/jats:p>","DOI":"10.3390\/informatics11020031","type":"journal-article","created":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T06:53:49Z","timestamp":1715928829000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6283-8197","authenticated-orcid":false,"given":"Franklin","family":"Parrales-Bravo","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial, Facultad de Ciencias Matem\u00e1ticas y F\u00edsicas, Universidad de Guayaquil, Guayaquil 090514, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0737-9132","authenticated-orcid":false,"given":"Rosangela","family":"Caicedo-Quiroz","sequence":"additional","affiliation":[{"name":"Centro de Estudios para el Cuidado Integral y la Promoci\u00f3n de la Salud, Universidad Bolivariana del Ecuador, Km 5 \u00bd v\u00eda Dur\u00e1n\u2014Yaguachi, Dur\u00e1n 092405, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1766-5626","authenticated-orcid":false,"given":"Elianne","family":"Rodr\u00edguez-Larraburu","sequence":"additional","affiliation":[{"name":"Facultad de Salud y Servicios Sociales, Instituto Superior Universitario Bolivariano de Tecnolog\u00eda, Guayaquil 090313, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2732-979X","authenticated-orcid":false,"given":"Julio","family":"Barzola-Monteses","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Inteligencia Artificial, Facultad de Ciencias Matem\u00e1ticas y F\u00edsicas, Universidad de Guayaquil, Guayaquil 090514, Ecuador"},{"name":"Centro de Estudios en Tecnolog\u00edas Aplicadas, Universidad Bolivariana del Ecuador, Km 5 \u00bd v\u00eda Dur\u00e1n\u2014Yaguachi, Dur\u00e1n 092405, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1161\/HYPERTENSIONAHA.117.10318","article-title":"Assessment of the fullPIERS risk prediction model in women with early-onset preeclampsia","volume":"71","author":"Ukah","year":"2018","journal-title":"Hypertension"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Parrales-Bravo, F., Saltos-Cede\u00f1o, J., Tomal\u00e1-Esparza, J., and Barzola-Monteses, J. (2023, January 19\u201321). Clustering-based Approach for Characterization of Patients with Preeclampsia using a Non-Redundant Feature Selection. Proceedings of the 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Spain.","DOI":"10.1109\/ICECCME57830.2023.10252898"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Koulouraki, S., Paschos, V., Pervanidou, P., Christopoulos, P., Gerede, A., and Eleftheriades, M. (2023). Short- and Long-Term Outcomes of Preeclampsia in Offspring: Review of the Literature. Children, 10.","DOI":"10.3390\/children10050826"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2200879","DOI":"10.1080\/14767058.2023.2200879","article-title":"Persisting risk factors for preeclampsia among high-risk pregnancies already using prophylactic aspirin: A multi-country retrospective investigation","volume":"36","author":"Muldoon","year":"2023","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Moreira, M.W., Rodrigues, J.J., Oliveira, A.M., Ramos, R.F., and Saleem, K. (2016, January 22\u201327). A preeclampsia diagnosis approach using Bayesian networks. Proceedings of the 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICC.2016.7510893"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1144170","DOI":"10.3389\/fmed.2023.1144170","article-title":"Preeclampsia pathophysiology and adverse outcomes during pregnancy and postpartum","volume":"10","author":"Bisson","year":"2023","journal-title":"Front. Med."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"ACOG (2020). Gestational Hypertension and Preeclampsia: ACOG Practice Bulletin. Obstet. Gynecol., 135, e237\u2013e260.","DOI":"10.1097\/AOG.0000000000003891"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chang, K.J., Seow, K.M., and Chen, K.H. (2023). Preeclampsia: Recent Advances in Predicting, Preventing, and Managing the Maternal and Fetal Life-Threatening Condition. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20042994"},{"key":"ref_9","unstructured":"Ministerio de Salud P\u00fablica del Ecuador (2022, March 28). Gaceta de Muerte Materna SE14, Available online: https:\/\/bit.ly\/3Poz79o."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.preghy.2019.03.005","article-title":"Prediction models for preeclampsia: A systematic review","volume":"16","author":"Hirst","year":"2019","journal-title":"Pregnancy Hypertens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.bpobgyn.2019.06.005","article-title":"Immunomodulation and preeclampsia","volume":"60","author":"Rambaldi","year":"2019","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"S1108","DOI":"10.1016\/j.ajog.2020.08.045","article-title":"Prevention of preeclampsia with aspirin","volume":"226","author":"Rolnik","year":"2020","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100100","DOI":"10.1016\/j.ajogmf.2020.100100","article-title":"Early prediction of preeclampsia via machine learning","volume":"2","author":"Tsur","year":"2020","journal-title":"Am. J. Obstet. Gynecol. MFM"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Parrales-Bravo, F., Torres-Urresto, J., Avila-Maldonado, D., and Barzola-Monteses, J. (2021, January 12\u201315). Relevant and Non-Redundant Feature Subset Selection Applied to the Detection of Malware in a Network. Proceedings of the 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM), Cuenca, Ecuador.","DOI":"10.1109\/ETCM53643.2021.9590777"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gopika, N., and ME, A.M.K. (2018, January 15\u201316). Correlation based feature selection algorithm for machine learning. Proceedings of the 2018 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/CESYS.2018.8723980"},{"key":"ref_16","first-page":"3","article-title":"A review of feature selection and its methods","volume":"19","author":"Venkatesh","year":"2019","journal-title":"Cybern. Inf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aljameel, S.S., Alzahrani, M., Almusharraf, R., Altukhais, M., Alshaia, S., Sahlouli, H., Aslam, N., Khan, I.U., Alabbad, D.A., and Alsumayt, A. (2023). Prediction of preeclampsia using machine learning and deep learning models: A review. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7010032"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1016\/j.neucom.2021.01.138","article-title":"Bayesian networks for interpretable machine learning and optimization","volume":"456","author":"Bielza","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102108","DOI":"10.1016\/j.artmed.2021.102108","article-title":"A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future","volume":"117","author":"Kyrimi","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"McLachlan, S., Daley, B., Saidi, S., Kyrimi, E., Dube, K., Grossan, C., Neil, M., Rose, L., and Fenton, N. (2024). Approach and Method for Bayesian Network Modelling: A Case Study in Pregnancy Outcomes for England and Wales. medRxiv.","DOI":"10.1101\/2024.01.06.24300925"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8682","DOI":"10.1038\/s41598-023-35838-6","article-title":"Mode of delivery and maternal vitamin D deficiency: An optimized intelligent Bayesian network algorithm analysis of a stratified randomized controlled field trial","volume":"13","author":"Amiri","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Moreira, M.W., Rodrigues, J.J., Oliveira, A.M., and Saleem, K. (2016, January 11\u201313). Smart mobile system for pregnancy care using body sensors. Proceedings of the 2016 International Conference on Selected Topics in Mobile & Wireless Networking (MoWNeT), Cairo, Egypt.","DOI":"10.1109\/MoWNet.2016.7496609"},{"key":"ref_23","first-page":"351","article-title":"Prediction of pre-eclampsia by maternal characteristics: A case-controlled validation study of a Bayesian network model for risk identification of pre-eclampsia","volume":"27","author":"Velikova","year":"2014","journal-title":"J. Matern. Fetal Neonatal Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.ijar.2013.03.016","article-title":"Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare","volume":"55","author":"Velikova","year":"2014","journal-title":"Int. J. Approx. Reason."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Velikova, M., Lucas, P.J., and Spaanderman, M. (2011, January 2\u20136). A predictive Bayesian network model for home management of preeclampsia. Proceedings of the Conference on Artificial Intelligence in Medicine in Europe, Bled, Slovenia.","DOI":"10.1007\/978-3-642-22218-4_22"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"455","DOI":"10.32614\/RJ-2018-073","article-title":"bnclassify: Learning Bayesian network classifiers","volume":"10","author":"Mihaljevic","year":"2018","journal-title":"R J."},{"key":"ref_27","first-page":"309","article-title":"The performance of Bayesian network classifiers for predicting discrete data","volume":"33","author":"Park","year":"2020","journal-title":"Korean J. Appl. Stat."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"030019","DOI":"10.1063\/5.0007885","article-title":"Reviewing the consistency of the Na\u00efve Bayes Classifier\u2019s performance in medical diagnosis and prognosis problems","volume":"Volume 2242","author":"Fauziyyah","year":"2020","journal-title":"Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences (ISCPMS2019)"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1007\/s00500-020-05297-6","article-title":"Naive Bayes: Applications, variations and vulnerabilities: A review of literature with code snippets for implementation","volume":"25","author":"Wickramasinghe","year":"2021","journal-title":"Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rivas, J.J., Orihuela-Espina, F., and Sucar, L.E. (2019, January 20\u201323). Recognition of affective states in virtual rehabilitation using late fusion with Semi-Naive Bayesian classifier. Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, Trento Italy.","DOI":"10.1145\/3329189.3329222"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1007465528199","article-title":"Bayesian network classifiers","volume":"29","author":"Friedman","year":"1997","journal-title":"Mach. Learn."},{"key":"ref_32","unstructured":"Pazzani, M.J. (1998). Feature Extraction, Construction and Selection: A Data Mining Perspective, Springer Science & Business Media."},{"key":"ref_33","unstructured":"Spasova Dimitrova, R. (2024, March 08). Desarrollo y evaluaci\u00f3n de m\u00e9todos de selecci\u00f3n de caracter\u00edsticas para la predicci\u00f3n de eventos adversos en pacientes polimedicados. Universidad P\u00fablica de Navarra. Available online: https:\/\/hdl.handle.net\/2454\/24594."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genom."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bravo, F.P., Garc\u00eda, A.A., Russo, L., and Ayala, J.L. (2020). SOFIA: Selection of Medical Features by Induced Alterations in Numeric Labels. Electronics, 9.","DOI":"10.3390\/electronics9091492"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"92598","DOI":"10.1109\/ACCESS.2019.2927429","article-title":"SMURF: Systematic Methodology for Unveiling Relevant Factors in retrospective data on chronic disease treatments","volume":"7","author":"Bravo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1093\/bioinformatics\/btr597","article-title":"MissForest\u2014non-parametric missing value imputation for mixed-type data","volume":"28","author":"Stekhoven","year":"2012","journal-title":"Bioinformatics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1093\/aje\/kwt312","article-title":"Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study","volume":"179","author":"Shah","year":"2014","journal-title":"Am. J. Epidemiol."},{"key":"ref_39","unstructured":"Arias-Mu\u00f1oz, A.C. (2024, March 08). Propuesta y evaluaci\u00f3n de una estrategia para la imputaci\u00f3n m\u00faltiple y multivariada de valores faltantes en series de tiempo del campo meteorol\u00f3gico utilizando aprendizaje autom\u00e1tico= Proposal and evaluation of a strategy for multiple and multivariate imputaci\u00f3n of missing values in time series of the meteorological field using machine learning. Instituto Tecnol\u00f3gico de Costa Rica. Available online: https:\/\/hdl.handle.net\/2238\/14060."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Alkabbani, H., Ramadan, A., Zhu, Q., and Elkamel, A. (2022). An improved air quality index machine learning-based forecasting with multivariate data imputation approach. Atmosphere, 13.","DOI":"10.3390\/atmos13071144"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, S., Gong, L., Zeng, Q., Li, W., Xiao, F., and Lei, J. (2021). Imputation of gps coordinate time series using missforest. Remote Sens., 13.","DOI":"10.3390\/rs13122312"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"P\u00e1rraga-Valle, J., Garc\u00eda-Berm\u00fadez, R., Rojas, F., Torres-Mor\u00e1n, C., and Sim\u00f3n-Cuevas, A. (2020, January 6\u20138). Evaluating mutual information and chi-square metrics in text features selection process: A study case applied to the text classification in PubMed. Proceedings of the Bioinformatics and Biomedical Engineering: 8th International Work-Conference, IWBBIO 2020, Granada, Spain.","DOI":"10.1007\/978-3-030-45385-5_57"},{"key":"ref_43","first-page":"745","article-title":"Ensemble Method of Feature Selection Using Filter and Wrapper Techniques with Evolutionary Learning","volume":"Volume 2","author":"Mukherjee","year":"2022","journal-title":"Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2022, Kolkata, India, 23\u201325 February 2022"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"104216","DOI":"10.1016\/j.engappai.2021.104216","article-title":"Supervised feature selection techniques in network intrusion detection: A critical review","volume":"101","author":"Galatro","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"R\u00e1cz, A., Bajusz, D., and H\u00e9berger, K. (2021). Effect of dataset size and train\/test split ratios in QSAR\/QSPR multiclass classification. Molecules, 26.","DOI":"10.3390\/molecules26041111"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100178","DOI":"10.1016\/j.health.2023.100178","article-title":"A comparative assessment of most widely used machine learning classifiers for analysing and classifying autism spectrum disorder in toddlers and adolescents","volume":"3","author":"Talukdar","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_47","first-page":"1","article-title":"Influence of data splitting on performance of machine learning models in prediction of shear strength of soil","volume":"2021","author":"Nguyen","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"132911","DOI":"10.1109\/ACCESS.2020.3009843","article-title":"CICIDS-2017 dataset feature analysis with information gain for anomaly detection","volume":"8","author":"Stiawan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fallucchi, F., Coladangelo, M., Giuliano, R., and William De Luca, E. (2020). Predicting employee attrition using machine learning techniques. Computers, 9.","DOI":"10.3390\/computers9040086"},{"key":"ref_50","unstructured":"BayesFusion, L. (2023, May 16). BayesFusion Modeler. User Manual. Available online: https:\/\/support.bayesfusion.com\/docs\/."},{"key":"ref_51","unstructured":"BayesFusion, L. (2024, March 08). Welcome to BayesFusion Website. BayesFusion, LLC. Available online: https:\/\/www.bayesfusion.com\/."},{"key":"ref_52","first-page":"2367","article-title":"Risk Factors Associated with Preeclampsia: A Case Control Study","volume":"9","author":"Singla","year":"2022","journal-title":"Eur. J. Mol. Clin. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1080\/14397595.2020.1830466","article-title":"The effect of parity, history of preeclampsia, and pregnancy care on the incidence of subsequent preeclampsia in multiparous women with SLE","volume":"31","author":"Maeda","year":"2021","journal-title":"Mod. Rheumatol."},{"key":"ref_54","first-page":"426","article-title":"Adverse outcomes of preeclampsia in previous and subsequent pregnancies and the risk of recurrence","volume":"55","author":"Coban","year":"2021","journal-title":"Med. Bull. Sisli Etfal Hosp."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1111\/aogs.14076","article-title":"Can information regarding the index stillbirth determine risk of adverse outcome in a subsequent pregnancy? Findings from a single-center cohort study","volume":"100","author":"Graham","year":"2021","journal-title":"Acta Obstet. Gynecol. Scand."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1515\/jpm-2022-0080","article-title":"The prevalence of and risk factors for stillbirths in women with severe preeclampsia in a high-burden setting at Mpilo Central Hospital, Bulawayo, Zimbabwe","volume":"50","author":"Ngwenya","year":"2022","journal-title":"J. Perinat. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"153206","DOI":"10.1016\/j.tox.2022.153206","article-title":"Polycyclic aromatic hydrocarbons (PAHs) may explain the paradoxical effects of cigarette use on preeclampsia (PE)","volume":"473","author":"Holme","year":"2022","journal-title":"Toxicology"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.jogc.2020.08.010","article-title":"Family history of hypertension, cardiovascular disease, or diabetes and risk of developing preeclampsia: A systematic review","volume":"43","author":"Kay","year":"2021","journal-title":"J. Obstet. Gynaecol. Can."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"18249","DOI":"10.1038\/s41598-020-75534-3","article-title":"Severe preeclampsia is associated with a higher relative abundance of Prevotella bivia in the vaginal microbiota","volume":"10","author":"Lin","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1038\/s41598-018-36964-2","article-title":"Association between preterm delivery and bacterial vaginosis with or without treatment","volume":"9","author":"Shimaoka","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_61","first-page":"191","article-title":"Maternal perinatal outcomes related to advanced maternal age in preeclampsia pregnant women","volume":"13","author":"Tyas","year":"2019","journal-title":"J. Fam. Reprod. Health"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/10641963.2018.1523919","article-title":"Assessment of occurrence of preeclampsia and some clinical and demographic risk factors in Zahedan city in 2017","volume":"41","author":"Farzaneh","year":"2019","journal-title":"Clin. Exp. Hypertens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Mattsson, K., Ju\u00e1rez, S., and Malmqvist, E. (2022). Influence of socio-economic factors and region of birth on the risk of preeclampsia in Sweden. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19074080"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1177\/1933719118804412","article-title":"Adolescent preeclampsia: Pathological drivers and clinical prevention","volume":"26","author":"Brosens","year":"2019","journal-title":"Reprod. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.preghy.2019.09.004","article-title":"Prediction of adverse maternal outcomes in preeclampsia at term","volume":"18","author":"Paul","year":"2019","journal-title":"Pregnancy Hypertens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.preghy.2019.09.003","article-title":"Worrying yourself sick? Association between pre-eclampsia onset and health-related worry in pregnancy","volume":"18","author":"Krishnamurti","year":"2019","journal-title":"Pregnancy Hypertens."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ruz, G.A., Henr\u00edquez, P.A., and Mascare\u00f1o, A. (2022). Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers. Mathematics, 10.","DOI":"10.3390\/math10020166"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Salman, I. (2020, January 28\u201330). Learning the Structure of the Tree and Tree Augmented Naive Bayesian from Incomplete and Imbalanced Data. Proceedings of the 2020 21st International Arab Conference on Information Technology (ACIT), Giza, Egypt.","DOI":"10.1109\/ACIT50332.2020.9300091"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wester, P., Heiding, F., and Lagerstr\u00f6m, R. (2021, January 25\u201329). Anomaly-based intrusion detection using tree augmented naive bayes. Proceedings of the 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), Gold Coast, Australia.","DOI":"10.1109\/EDOCW52865.2021.00040"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/11\/2\/31\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:43:56Z","timestamp":1760107436000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/11\/2\/31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,17]]},"references-count":69,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["informatics11020031"],"URL":"https:\/\/doi.org\/10.3390\/informatics11020031","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2024,5,17]]}}}