{"doi":"10.1016/j.imu.2021.100533","title":"Classification of intrauterine growth restriction at 34–38 weeks gestation with machine learning models","abstract":"OBJECTIVE: Intrauterine growth restriction (IUGR) is one of the most common causes of stillbirths. The objective of this study is to develop a machine learning model that will be able to accurately and consistently predict whether the estimated fetal weight (EFW) will be below the 10th percentile at 34+0-37 + 6 week's gestation stage, by using data collected at 20 + 0 to 23 + 6 weeks gestation. METHODS: Recruitment for the prospective Safe Passage Study (SPS) was done over 7.5 years (2007-2015). An essential part of the fetal assessment was the non-invasive transabdominal recording of the maternal and fetal electrocardiograms as well as the performance of an ultrasound examination for Doppler flow velocity waveforms and fetal biometry at 20 + 0 to 23 + 6 and 34 + 0 to 37 + 6 week's gestation. Several predictive models were constructed, using supervised learning techniques, and evaluated using the Stochastic Gradient Descent, k-Nearest Neighbours, Logistic Regression and Random Forest methods. RESULTS: The final model performed exceptionally well across all evaluation metrics, particularly so for the Stochastic Gradient Descent method: achieving a 93% average for Classification Accuracy, Recall, Precision and F1-Score when random sampling is used and 91% for cross-validation (both methods using a 95% confidence interval). Furthermore, the model identifies the Umbilical Artery Pulsality Index to be the strongest identifier for the prediction of IUGR - matching the literature. Three of the four evaluation methods used achieved above 90% for both True Negative and True Positive results. The ROC Analysis showed a very strong True Positive rate (y-axis) for both target attribute outcomes - AUC value of 0.771. CONCLUSIONS: The model performs exceptionally well in all evaluation metrics, showing robustness and flexibility as a predictive model for the binary target attribute of IUGR. This accuracy is likely due to the value added by the pre-processed features regarding the fetal gained beats and accelerations, something otherwise absent from previous multi-disciplinary studies. The success of the proposed predictive model allows the pursuit of further birth-related anomalies, providing a foundation for more complex models and lesser-researched subject matter. The data available for this model was a vital part of its success but might also become a limiting factor for further analyses. Further development of similar models could result in better classification performance even with little data available.","journal":"Informatics in Medicine Unlocked","year":2021,"id":173189,"datarank":0.48283137373023016,"base_score":3.2188758248682006,"endowment":3.2188758248682006,"self_citation_contribution":0.48283137373023016,"citation_network_contribution":0.0,"self_endowment_contribution":0.48283137373023016,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":24,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.911,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":471660,"name":"Lucy Brink","orcid":"0000-0001-6737-8225","position":1,"is_corresponding":false},{"id":710936,"name":"C Du Plessis","orcid":null,"position":2,"is_corresponding":false},{"id":298552,"name":"Hein J. Odendaal","orcid":"0000-0001-5672-2000","position":3,"is_corresponding":false},{"id":710338,"name":"Ivan Crockart","orcid":"0000-0002-0914-0224","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:46:45.768135Z","pmid":"34007875","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}