Environmental Factors Prediction in Preterm Birth Using Comparison between Logistic Regression and Decision Tree Methods:  An Exploratory Analysis

Abstract

Background and aimPreterm birth is one of the major cause 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.MethodWe have used 90 pregnant mothers, of whom 40 are preterm and 50 are full-term births. We have checked the model accuracy of the dataset through logistic regression and decision tree classifier model.ResultsThe 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).ConclusionsThe 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).