An Assessment Of The Performance Of Discriminant Analysis And The Logistic Regression Methods In Classification Of Mode Of Delivery Of An Expectant Mother

Abstract

The study compares two statistical methods: Discriminant analysis and the Logistic regression model in predicting Mode of Delivery of an expectant mother, Natural birth and Caesarian section. Of the 184 cases examined for Mode of Delivery of an expectant mother, Discriminant Analysis classified the Natural birth correctly (64.6%) while it recorded (64.7%) success rate in classifying the Caesarian section. In the case of the Logistic regression, it recorded (76.8%) and (52.9%) success rate in classifying the Natural birth and Caesarian section respectively. The overall predictive performance of the two models was high with the Logistic regression having the highest value (64.7%) and (65.8%) for Discriminant Analysis. Among the five characteristics examined, Mothers height, Baby’s weight and gender were not significant variables for identifying Mode of delivery by both methods while Mothers weight is important identifying variable for both except Mothers age which was significant in the Discriminant analysis. The study shows that both techniques estimated almost the same statistical significant coefficient and that the overall classification rate for both was good while either can be helpful in selection of Mode of delivery for an expectant mother. However, given the failure rate to meet the underlying assumptions of Discriminant Analysis, Logistic Regression is preferable.

Keywords: Logistic Regression, Classification, Mode of delivery, Discriminant Analysis.