{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:51:01Z","timestamp":1790833861693,"version":"4.1.0"},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer\u2019s output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning.<\/jats:p>","DOI":"10.3390\/diagnostics13152471","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:03:25Z","timestamp":1690333405000},"page":"2471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Early Diagnosis and Classification of Fetal Health Status from a Fetal Cardiotocography Dataset Using Ensemble Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Adem","family":"Kuzu","sequence":"first","affiliation":[{"name":"Department of Software Engineering, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8942-4605","authenticated-orcid":false,"given":"Yunus","family":"Santur","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Engineering, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1097\/AOG.0000000000004010","article-title":"Rates of maternal and perinatal mortality and vertical transmission in pregnancies complicated by severe acute respiratory syndrome coronavirus 2 (SARS-Co-V-2) infection: A systematic review","volume":"136","author":"Huntley","year":"2020","journal-title":"Obstet. Gynecol."},{"key":"ref_2","unstructured":"(2022, December 01). World Health Organization. Available online: http:\/\/whqlibdoc.who.int\/publications\/2006\/9241563206_eng.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5506","DOI":"10.1080\/14767058.2021.1887120","article-title":"Computerized analysis of fetal heart rate pattern in the third trimester of low-risk pregnancy by long-range electronic fetal monitoring","volume":"35","author":"Li","year":"2021","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1080\/14767058.2020.1711727","article-title":"Association between adverse perinatal outcomes and sleep disturbances during pregnancy: A systematic review and meta-analysis","volume":"35","author":"Yang","year":"2022","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"159","DOI":"10.4274\/tjod.galenos.2021.90023","article-title":"Perinatal outcomes of high-dose vitamin D administration in the last trimester","volume":"18","author":"Kucukali","year":"2021","journal-title":"Turk. J. Obstet. Gynecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1089\/jmf.2020.0076","article-title":"Thyme (Thymus vulgaris [Lamiaceae]) Leaves Inhibit Contraction of the Nonpregnant Mouse Uterus","volume":"24","author":"Bafor","year":"2021","journal-title":"J. Med. Food"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1080\/14767058.2019.1666819","article-title":"Physiological CTG interpretation: The significance of baseline fetal heart rate changes after the onset of decelerations and associated perinatal outcomes","volume":"34","author":"Jia","year":"2021","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3977","DOI":"10.18203\/2320-1770.ijrcog20204021","article-title":"Study of umbilical coiling index and perinatal outcome","volume":"9","author":"Singh","year":"2020","journal-title":"Int. J. Reprod. Contracept. Obstet. Gynecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.procs.2020.02.248","article-title":"Classification of the cardiotocograph data for anticipation of fetal risks using bagging ensemble classifier","volume":"168","author":"Subasi","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"181450","DOI":"10.1109\/ACCESS.2020.3028333","article-title":"Artificial intelligence in pregnancy: A scoping review","volume":"8","author":"Oprescu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e12379","DOI":"10.1111\/exsy.12379","article-title":"Architecture and implementation of a smart-pregnancy monitoring system using web-based application","volume":"37","author":"Santur","year":"2020","journal-title":"Expert Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4394","DOI":"10.1038\/s41598-020-61357-9","article-title":"Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning","volume":"10","author":"Farias","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5587","DOI":"10.1080\/14767058.2021.1887847","article-title":"Ultrasound placental image texture analysis using artificial intelligence to predict hypertension in pregnancy","volume":"35","author":"Gupta","year":"2021","journal-title":"J. Matern.-Fetal Neonatal Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"21","DOI":"10.53375\/ijecer.2021.25","article-title":"\u201cMumCare\u201d: An Artificial Intelligence Based Assistant","volume":"1","author":"Maduwantha","year":"2021","journal-title":"Int. J. Electr. Comput. Eng. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1016\/j.procs.2021.01.226","article-title":"mHealth for Self-Management in Pregnancy: Perceptions of Women in Low-Resource Settings","volume":"181","author":"Dansharif","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1007\/s10815-021-02272-4","article-title":"Artificial intelligence\u2014The future is now","volume":"38","author":"Trolice","year":"2021","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_18","unstructured":"(2023, July 01). UCI CTG Dataset. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/cardiotocography."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., and Belongie, S. (2019, January 16\u201317). Class-balanced loss based on effective number of samples. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8008","DOI":"10.1109\/TIP.2021.3112012","article-title":"Domain adaptive ensemble learning","volume":"30","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","unstructured":"Montgomery, D.C., Peck, E.A., and Vining, G.G. (2021). Introduction to Linear Regression Analysis, John Wiley & Sons."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110210","DOI":"10.1016\/j.chaos.2020.110210","article-title":"Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm","volume":"140","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4145","DOI":"10.1007\/s00366-021-01393-9","article-title":"Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration","volume":"38","author":"Qiu","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.ins.2019.06.064","article-title":"Three-way confusion matrix for classification: A measure driven view","volume":"507","author":"Xu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s42979-021-00694-6","article-title":"A Shallow 1-D Convolution Neural Network for Fetal State Assessment Based on Cardiotocogram","volume":"2","author":"Fasihi","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Piri, J., Mohapatra, P., and Dey, R. (2021, January 2\u20134). Multi-objective Ant Lion Optimization Based Feature Retrieval Methodology for Investigation of Fetal Wellbeing. Proceedings of the 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India.","DOI":"10.1109\/ICIRCA51532.2021.9544860"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6656770","DOI":"10.1155\/2021\/6656770","article-title":"Intelligent Neutrosophic Diagnostic System for Cardiotocography Data","volume":"2021","author":"Amin","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"62","DOI":"10.46291\/ICONTECHvol5iss2pp62-70","article-title":"Multi-Classification of Fetal Health Status Using Extreme Learning Machine","volume":"5","year":"2021","journal-title":"Icontech Int. J."},{"key":"ref_29","first-page":"30","article-title":"Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning","volume":"5","author":"Bhowmik","year":"2021","journal-title":"Inf. Technol. Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Haweel, M.T., Zahran, O., and Abd El-Samie, F.E. (2021, January 18\u201321). Polynomial FLANN Classifier for Fetal Cardiotocography Monitoring. Proceedings of the 2021 38th National Radio Science Conference (NRSC), Mansoura, Egypt.","DOI":"10.1109\/NRSC52299.2021.9509832"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fei, Y., Huang, X., Chen, Q., Chen, J., Li, L., Hong, J., and Wei, H. (2021, January 16\u201319). Automatic Classification of Antepartum Cardiotocography Using Fuzzy Clustering and Adaptive Neuro-Fuzzy Inference System. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Republic of Korea.","DOI":"10.1109\/BIBM49941.2020.9313143"},{"key":"ref_32","first-page":"7","article-title":"Classification and Feature Selection Approaches for Cardiotocography by Machine Learning Techniques","volume":"12","author":"Nandipati","year":"2020","journal-title":"J. Telecommun. Electron. Comput. Eng. (JTEC)"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"John, M., and Shaiba, H. (2020, January 4\u20135). Ensemble based foetal state diagnosis. Proceedings of the 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), Riyadh, Saudi Arabia.","DOI":"10.1109\/CDMA47397.2020.00028"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Piri, J., Mohapatra, P., and Dey, R. (2020, January 2\u20134). Fetal Health Status Classification Using MOGA-CD Based Feature Selection Approach. Proceedings of the 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT50063.2020.9198377"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, J.Y., Liu, X.C., Wei, H., Chen, Q.Q., Hong, J.M., Li, Q.N., and Hao, Z.F. (2019, January 1\u20132). Imbalanced Cardiotocography Multi-classification for Antenatal Fetal Monitoring Using Weighted Random Forest. Proceedings of the International Conference on Smart Health, Shenzhen, China.","DOI":"10.1007\/978-3-030-34482-5_7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sevani, N., Hermawan, I., and Jatmiko, W. (2019, January 22\u201324). Feature Selection based on F-score for Enhancing CTG Data Classification. Proceedings of the 2019 IEEE International Conference on Cybernetics and Computational Intelligence, Banda Aceh, Indonesia.","DOI":"10.1109\/CYBERNETICSCOM.2019.8875656"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105854","DOI":"10.1016\/j.asoc.2019.105854","article-title":"Stacked autoencoder based deep random vector functional link neural network for classification","volume":"85","author":"Katuwal","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_38","first-page":"8564","article-title":"Weighted deep neural network based clinical decision support system for the determination of fetal health","volume":"8","author":"Vani","year":"2019","journal-title":"Int. J. Recent Technol. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.artmed.2019.03.005","article-title":"Prediction of fetal state from the cardiotocogram recordings using neural network models","volume":"96","author":"Iraji","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_40","unstructured":"Uzun, A., K\u0131z\u0131ltas, C.E., and Y\u0131lmaz, E. (2018, January 11\u201313). Cardiotocography data set classification with extreme learning machine. Proceedings of the International Conference on Advanced Technologies, Safranbolu, Turkey."},{"key":"ref_41","first-page":"29","article-title":"Prediction of fetal health state during pregnancy: A survey","volume":"6","author":"Deressa","year":"2018","journal-title":"Int. J. Comput. Sci. Trends Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e12899","DOI":"10.1111\/exsy.12899","article-title":"Fetal health classification from cardiotocographic data using machine learning","volume":"39","author":"Mehbodniya","year":"2022","journal-title":"Expert Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kaliappan, J., Bagepalli, A.R., Almal, S., Mishra, R., Hu, Y.C., and Srinivasan, K. (2023). Impact of Cross-Validation on Machine Learning Models for Early Detection of Intrauterine Fetal Demise. Diagnostics, 13.","DOI":"10.3390\/diagnostics13101692"},{"key":"ref_44","first-page":"752","article-title":"A Method for Predicting and Classifying Fetus Health Using Machine Learning","volume":"11","author":"Shruthi","year":"2023","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Paul, R.K., Yeasin, M., Kumar, P., Kumar, P., Balasubramanian, M., Roy, H.S., and Gupta, A. (2022). Machine learning techniques for forecasting agricultural prices: A case of brinjal in Odisha, India. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0270553"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/15\/2471\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:18:35Z","timestamp":1760127515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/15\/2471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":45,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["diagnostics13152471"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13152471","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,25]]}}}