Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study

Abstract

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.

Keywords: Cerebral Palsy; Data Collection; Infant; Neurology; Statistics.

Figure 1. Comparison of mean ROC, sensitivity and specificity values of four ML algorithms: stochastic gradient boosting, regularised logistic regression, random forest and k-nearest neighbour. Bars represent 95% CIs. ML, machine learning; ROC, receiver operating characteristic.

Figure 2. The most important variables of the final model after the incorporation of three additional random variables into the dataset. The variables are ordered from most to least important and scaled to have a maximum value of 100. BMI, body mass index; CTG, cardiotocography; GA, gestational age; IVH, intraventricular haemorrhage.