A decision tree model of cerebral palsy based on risk factors
Shiting Xiang1, Liping Li1, Lili Wang1
1Paediatric Medicine Institution of Hunan Children's Hospital, Changsha, China.
Insights
A decision tree model effectively predicts individual cerebral palsy (CP) risk using factors like preterm birth and birth asphyxia. This tool aids in early identification and intervention for CP.
Area of Science:
- Pediatrics
- Neurology
- Public Health
Background:
- Cerebral palsy (CP) is a significant developmental disorder affecting motor function.
- Accurate risk prediction is crucial for timely intervention and improved outcomes.
Purpose of the Study:
- To establish a decision tree model for predicting individual cerebral palsy (CP) risk.
- To identify key risk factors associated with CP.
Main Methods:
- A hospital-based case-control study involving 109 CP cases and 327 controls.
- Data collected via questionnaires and face-to-face interviews.
- Decision tree modeling used for prediction, with Chi-square tests for factor identification.
Main Results:
- Significant risk factors identified: preterm birth, birth asphyxia, and maternal age over 35.
- The decision tree model achieved an AUC of 0.722 (p < .001).
- Factors like maternal age, weight gain, medical treatment, low birth weight, and birth asphyxia showed significant differences between groups.
Conclusions:
- The developed decision tree model demonstrates utility in predicting individual CP risk.
- Further large-scale, population-based studies are recommended to refine the prediction model.
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.
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