Segmentation of Pregnant Women to Guide How Intervention Programs Are Formulated and Implemented to Ensure Positive Pregnancy Outcomes

Abstract

Interventions and schemes aimed at reducing the risks associated with pregnancy are often implemented wholesale. This often leads to misdirection of interventions to the wrong patients. To address this, there was the need to extract segments that subgroup maternal attributes into frequently occurring patterns. Some secondary data consisting of records of pregnant women who attended antenatal care (ANC) visits at a hospital were subjected to association rules mining. The analysis extracted and sub-grouped the attributes and characteristics of pregnant women that often co-occur. Segmentation was done in three sub-groups. Each segment consists of both positive and risk attributes/characteristics that often occur together in a pregnant woman. With the aid of the segments, intervention programs can be designed and delivered according to the needs and requirements of each segment. This provokes a new perspective on how quality healthcare service delivery can be channeled to pregnant women based on specific needs.