Environmental factors prediction in preterm birth using comparison between logistic regression and decision tree methods: An exploratory analysis

Background and aim: Preterm birth is one of the major causes of neonatal death in the developing countries and environmental factors are playing vital role in pre term birth. Nowadays Machine learning techniques are very useful for finding the hidden factors and classifications. The purpose of this study is to illustrate the importance of machine learning classification models and to identify the significant environmental factors behind pre-term birth. Method: Between 2017 and 2018, 90 pregnant females underwent birth outcome followed by research staff at our institutions, out of those 50 are full-term and 40 are preterm births in this study. Before and after feature selection logistic regression and decision tree classifier model has been compared in this dataset and to evaluate the model accuracy. Preforming the accuracy of machine learning classification model and important factors on pre-term birth. Results: Using chi-square test and find the Area of residence and GSH, MDA, α-HCH, total HCH and total DDT are responsible for the preterm birth. Using the multiple logistic regression, pre term birth was associated with MDA and α-HCH (95% CI 0.04 to 0.48 and 95% CI 0.82 to 0.97). The comparative outcome of the logistic and decision tree model reveals that logistic regression is stronger in terms of metrics (precision = 0.92, F1-score = 0.96 and AUROC = 0.97), while the weak result shows by the decision tree (precision = 0.75, F1-score = 0.86 and AUROC = 0.87). Conclusions: The conclusion shows that logistic regression is more appropriate as compare to decision tree classification model in the preterm birth data. The most influential factors for preterm birth are variables like α –HCH, total HCH and MDA (Malondialdehyde).

Abstract licence: CC BY-NC

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