A decision tree model of cerebral palsy based on risk factors

Abstract

Objective

A risk prediction model of cerebral palsy (CP) was established by a decision tree model to predict the individual risk of CP.

Methods

A hospital-based case-control study was conducted with 109 cases of CP and 327 controls without CP. The cases and the controls were obtained from Hunan Children’s Hospital. A questionnaire was administered to collect the variables relevant to CP by face to face interviews. Chi-square test was used to identify the factors associated with CP, and a decision tree model was used to construct the prediction model.

Results

Univariate analysis showed that there were significant differences between cases group and controls group on maternal age, weight gain during pregnancy, medical treatment during pregnancy, preterm birth, low birth weight and birth asphyxia (all p-values <.05). Three factors, including preterm birth, birth asphyxia, and maternal age >35 years old, entered the decision tree model. The area under the receiver operating characteristic curve (AUC) was 0.722 (95%CI: 0.659–0.784, p < .001).

Conclusion

The decision tree prediction model can be used for predicting the individual risk of CP. Further large-scale, population-based cerebral palsy studies are needed to improve the model.

Keywords:

• Cerebral palsy
• decision tree model
• risk factors

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

Shiting Xiang, Liping li, Juan Liu, Yaqiong Tan, JIhong Hu. Each author’s individual contributions are briefly explained as follows. JIhong Hu the corresponding author, conceived, designed, and led the research; Shiting Xiang completed the acquisition of data, designed both the study and its analytical strategy, performed the data analyses, and wrote the manuscript; Liping Li, Lili Wang, Juan Liu, Yaqiong Tan helped completed the acquisition of data.