Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression
Purpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? Objectives: To develop and internally validate a ML model to predict neurodevelopmental impairment (NDI) in very preterm infants (<31 week...
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MDPI AG
2024-12-01
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| author | Jehier Afifi Tahani Ahmad Alessandro Guida Michael John Vincer Samuel Alan Stewart |
| author_facet | Jehier Afifi Tahani Ahmad Alessandro Guida Michael John Vincer Samuel Alan Stewart |
| author_sort | Jehier Afifi |
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| description | Purpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? Objectives: To develop and internally validate a ML model to predict neurodevelopmental impairment (NDI) in very preterm infants (<31 weeks) at 36 months corrected age, using clinical predictors. Methods: A retrospective cohort of very preterm infants (23<sup>0–</sup>30<sup>6</sup> weeks) born between January 2004 and December 2016 in Nova Scotia, Canada. Survivors with neurodevelopmental assessment at 36 months corrected age were included. The study sample was randomly split (80:20) into a development and testing datasets. We compared four methods: LR, elastic net (EN), random forest ensemble (RF) and gradient boosting (XGB), in relation to discrimination (AUC), calibration, and diagnostic properties. Results: Of 811 eligible infants, 663 were included (mean gestational age 28 weeks, mean birth weight 1137 g and 52% male). Of those, 195 (29%) developed NDI and 468 (71%) did not. On internal validation using the testing dataset, all four models provided good discrimination of NDI with comparable AUC. RF was superior to the other three methods with a higher AUC (0.79 vs. 0.74, 0.74, and 0.73 for XGB, EN and LR, respectively), but all models have overlapped CIs. Conclusions: In this population-based cohort of very preterm infants, RF was superior to conventional LR in prediction of NDI at 3 years corrected age. Accurate prediction of preterm infants at risk of NDI enables early referrals for intervention programs and resources allocation toward those who are most likely to benefit. |
| format | Article |
| id | doaj-art-3b87ace4c27b461aa1cd8c7555d3a638 |
| institution | Kabale University |
| issn | 2227-9067 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Children |
| spelling | doaj-art-3b87ace4c27b461aa1cd8c7555d3a6382024-12-27T14:17:56ZengMDPI AGChildren2227-90672024-12-011112151210.3390/children11121512Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic RegressionJehier Afifi0Tahani Ahmad1Alessandro Guida2Michael John Vincer3Samuel Alan Stewart4Division of Neonatal Perinatal Medicine, Department of Pediatrics, Dalhousie University, Halifax, NS B3K 6R8, CanadaDepartment of Diagnostic Imaging, Dalhousie University, Halifax, NS B3K 6R8, CanadaDepartment of Diagnostic Imaging, Dalhousie University, Halifax, NS B3K 6R8, CanadaDivision of Neonatal Perinatal Medicine, Department of Pediatrics, Dalhousie University, Halifax, NS B3K 6R8, CanadaDepartment of Community Health & Epidemiology, Dalhousie University, Halifax, NS B3H 1V7, CanadaPurpose: Is machine learning (ML) superior to the traditionally used logistic regression (LR) in prediction of neurodevelopmental outcomes in preterm infants? Objectives: To develop and internally validate a ML model to predict neurodevelopmental impairment (NDI) in very preterm infants (<31 weeks) at 36 months corrected age, using clinical predictors. Methods: A retrospective cohort of very preterm infants (23<sup>0–</sup>30<sup>6</sup> weeks) born between January 2004 and December 2016 in Nova Scotia, Canada. Survivors with neurodevelopmental assessment at 36 months corrected age were included. The study sample was randomly split (80:20) into a development and testing datasets. We compared four methods: LR, elastic net (EN), random forest ensemble (RF) and gradient boosting (XGB), in relation to discrimination (AUC), calibration, and diagnostic properties. Results: Of 811 eligible infants, 663 were included (mean gestational age 28 weeks, mean birth weight 1137 g and 52% male). Of those, 195 (29%) developed NDI and 468 (71%) did not. On internal validation using the testing dataset, all four models provided good discrimination of NDI with comparable AUC. RF was superior to the other three methods with a higher AUC (0.79 vs. 0.74, 0.74, and 0.73 for XGB, EN and LR, respectively), but all models have overlapped CIs. Conclusions: In this population-based cohort of very preterm infants, RF was superior to conventional LR in prediction of NDI at 3 years corrected age. Accurate prediction of preterm infants at risk of NDI enables early referrals for intervention programs and resources allocation toward those who are most likely to benefit.https://www.mdpi.com/2227-9067/11/12/1512machine learningpredictive modelingpreterm infantsneurodevelopment |
| spellingShingle | Jehier Afifi Tahani Ahmad Alessandro Guida Michael John Vincer Samuel Alan Stewart Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression Children machine learning predictive modeling preterm infants neurodevelopment |
| title | Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression |
| title_full | Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression |
| title_fullStr | Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression |
| title_full_unstemmed | Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression |
| title_short | Prediction of Neurodevelopmental Outcomes in Very Preterm Infants: Comparing Machine Learning Methods to Logistic Regression |
| title_sort | prediction of neurodevelopmental outcomes in very preterm infants comparing machine learning methods to logistic regression |
| topic | machine learning predictive modeling preterm infants neurodevelopment |
| url | https://www.mdpi.com/2227-9067/11/12/1512 |
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