Cluster Analysis as a Prediction Tool for Pregnancy Outcomes

Ines Banjari, Daniela Kenjerić, Krešimir Šolić, Milena L. Mandić


Considering specific physiology changes during gestation and thinking of pregnancy as a “critical window”, classification of pregnant women at early pregnancy can be considered as crucial. The paper demonstrates use of a method based on an approach from intelligent data mining, the cluster analysis. Cluster analysis method is a statistical method which makes possible to group individuals based on sets of identifying variables. The method was chosen in order to determine possibility for classification of pregnant women at early pregnancy to analyze unknown correlations between different variables so that the certain outcomes could be predicted. 222 pregnant women from two general obstetric offices’ were recruited. The main orient was set on characteristics of these pregnant women: their age, pre-pregnancy body mass index (BMI) and haemoglobin value. Cluster analysis gained a 94.1% classification accuracy rate with three branches or groups of pregnant women showing statistically significant correlations with pregnancy outcomes. The results are showing that pregnant women both of older age and higher pre-pregnancy BMI have a significantly higher incidence of delivering baby of higher birth weight but they gain significantly less weight during pregnancy. Their babies are also longer, and these women have significantly higher probability for complication during pregnancy (gestosis) and higher probability of induced or caesarean delivery. We can conclude that the cluster analysis method can appropriately classify pregnant women at early pregnancy to predict certain outcomes.


pregnant women, cluster analysis, pre-pregnancy BMI, maternal age, pregnancy outcomes, caesarean delivery, gestosis, weight gain, birth weight, classification

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