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Identification of Latent Risk Clinical Attributes for Children Born Under IUGR Condition Using Machine Learning
Sau Nguyen Van1, J A Lobo Marques2, T A Biala3
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Insights
Machine learning accurately identifies long-term childhood development factors in children born with Intrauterine Growth Restriction (IUGR). This aids in monitoring growth and health outcomes years after birth.
Area of Science:
- Pediatric Health
- Biomedical Informatics
- Machine Learning in Medicine
Background:
- Intrauterine Growth Restriction (IUGR) impacts fetal development, with long-term childhood effects requiring further research.
- Existing research documents maternal and genetic causes of IUGR.
- The long-term developmental impact of IUGR necessitates advanced analytical approaches.
Purpose of the Study:
- To develop a machine learning (ML) model for identifying significant physiological, clinical, and socioeconomic factors associated with IUGR.
- To analyze long-term (10-year) developmental outcomes in children with a history of IUGR.
- To determine the importance of various features correlated with previous IUGR conditions.
Main Methods:
- Utilized a cohort of 41 IUGR and 34 Non-IUGR children, followed up for an average of 9.18 years.
- Applied ML algorithms to classify children based on 24-hour ECG (Holter) and blood pressure (ABPM) monitoring, alongside clinical and socioeconomic data.
- Implemented a feature relevance algorithm to assess the importance of predictive factors.
Main Results:
- Achieved a classification accuracy of up to 94.73%, outperforming seven state-of-the-art ML algorithms.
- Identified key factors including day-time heart rate, day-night systolic blood pressure, 24-hour SD of SBP, morning cortisol creatinine, and 24-hour SDNNi.
- Highlighted the significance of heart rate variability (HRV) and blood pressure (BP) monitoring data.
Conclusions:
- The developed ML classification system demonstrates high accuracy in identifying long-term IUGR impacts.
- The identified relevant attributes can assist medical teams in monitoring the childhood development of IUGR children.
- This approach offers valuable insights for clinical decision-making and personalized pediatric care.
Background And Objective:
Intrauterine Growth Restriction (IUGR) is a condition in which a fetus does not grow to the expected weight during pregnancy. There are several well documented causes in the literature for this issue, such as maternal disorder, and genetic influences. Nevertheless, besides the risk during pregnancy and labour periods, in a long term perspective, the impact of IUGR condition during the child development is an area of research itself. The main objective of this work is to propose a machine learning solution to identify the most significant features of importance based on physiological, clinical or socioeconomic factors correlated with previous IUGR condition after 10 years of birth.
Methods:
In this work, 41 IUGR (18 male) and 34 Non-IUGR (22 male) children were followed up 9 years after the birth, in average (9.1786 ± 0.6784 years old). A group of machine learning algorithms is proposed to classify children previously identified as born under IUGR condition based on 24-hours monitoring of ECG (Holter) and blood pressure (ABPM), and other clinical and socioeconomic attributes. In additional, an algorithm of relevance determination based on the classifier is also proposed, to determine the level of importance of the considered features.
Results:
The proposed classification solution achieved accuracy up to 94.73%, and better performance than seven state-of-the-art machine learning algorithms. Also, relevant latent factors related to HRV and BP monitoring are proposed, such as: day-time heart rate (day-time HR), day-night systolic blood pressure (day-night SBP), 24-hour standard deviation (SD) of SBP, dropped, morning cortisol creatinine, 24-hour mean of SDs of all NN intervals for each 5 minutes segment (24-hour SDNNi), among others.
Conclusion:
With outstanding accuracy of our proposed solutions, the classification system and the indication of relevant attributes may support medical teams on the clinical monitoring of IUGR children during their childhood development.
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