Research on the construction of prediction model for depressive symptom in the second and third trimester of pregnancy based on artificial neural network - Summary - MDSpire
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Research on the construction of prediction model for depressive symptom in the second and third trimester of pregnancy based on artificial neural network
To investigate the current status of depression in women in the second and third trimesters of pregnancy, analyze associated factors, and construct a predictive model for depressive symptoms to provide evidence for early screening and targeted intervention.
Key Findings:
The positive rate of depressive symptoms screening was 42.7%.
Social support, active coping, and reactive personality were negatively correlated with depressive symptoms (P<0.05).
Negative coping and susceptible vulnerable personality were positively correlated with depressive symptoms (P<0.05).
The logistic regression model had a prediction accuracy of 79.6%.
The artificial neural network model had a prediction accuracy of 86.9%.
Interpretation:
The study highlights the prevalence of depressive symptoms in pregnant women and identifies key factors influencing these symptoms, suggesting the need for nursing measures based on these factors.
Limitations:
Conclusion:
The findings support early identification and targeted intervention for depressive symptoms in the second and third trimesters of pregnancy, emphasizing the utility of the predictive model in clinical practice.
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