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Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study
Sara Rapuc1, Blaž Stres2,3, Ivan Verdenik4
1Department of Pediatric Neurology, University Children's Hospital, University Medical Centre Ljubljana, Ljubljana, Slovenia.
Insights
Machine learning models using early perinatal factors showed limited success in predicting cerebral palsy (CP). Further research incorporating genetic and neuroimaging data is recommended for improved prediction of CP.
Area of Science:
- Neurology
- Pediatrics
- Medical Informatics
Background:
- Cerebral palsy (CP) is a significant neurological disorder impacting child development.
- Identifying perinatal risk factors is crucial for developing effective prevention and treatment strategies for CP.
Purpose of the Study:
- To identify early predictors of cerebral palsy (CP) using machine learning (ML).
- To evaluate the efficacy of ML algorithms in predicting CP based on perinatal data.
Main Methods:
- A retrospective, population-based case-control study was conducted.
- Data from the Slovenian National Perinatal Information System and Registry of Cerebral Palsy were utilized.
- A stochastic gradient boosting ML model was developed and validated using 338 CP cases and 1014 controls, excluding those with congenital anomalies.
Main Results:
- The best-performing ML model achieved a mean ROC value of 0.81 (sensitivity=0.46, specificity=0.95) in the primary dataset.
- Validation of the model on a separate dataset yielded an area under the ROC curve of 0.77 (sensitivity=0.27, specificity=0.94).
Conclusions:
- The developed ML model, based on early perinatal factors, demonstrated insufficient reliability for predicting CP in the studied cohort.
- Future research should incorporate additional data, including genetic and neuroimaging information, to enhance CP prediction models.
Objective:
Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aimed to identify the early predictors of CP using machine learning (ML).
Design:
This is a retrospective case-control study, using data from the two population-based databases, the Slovenian National Perinatal Information System and the Slovenian Registry of Cerebral Palsy. Multiple ML algorithms were evaluated to identify the best model for predicting CP.
Setting:
This is a population-based study of CP and control subjects born into one of Slovenia's 14 maternity wards.
Participants:
A total of 382 CP cases, born between 2002 and 2017, were identified. Controls were selected at a control-to-case ratio of 3:1, with matched gestational age and birth multiplicity. CP cases with congenital anomalies (n=44) were excluded from the analysis. A total of 338 CP cases and 1014 controls were included in the study.
Exposure:
135 variables relating to perinatal and maternal factors.
Main Outcome Measures:
Receiver operating characteristic (ROC), sensitivity and specificity.
Results:
The stochastic gradient boosting ML model (271 cases and 812 controls) demonstrated the highest mean ROC value of 0.81 (mean sensitivity=0.46 and mean specificity=0.95). Using this model with the validation dataset (67 cases and 202 controls) resulted in an area under the ROC curve of 0.77 (mean sensitivity=0.27 and mean specificity=0.94).
Conclusions:
Our final ML model using early perinatal factors could not reliably predict CP in our cohort. Future studies should evaluate models with additional factors, such as genetic and neuroimaging data.
