A novel health monitoring approach for pregnant women

Pregnancy complications are a leading cause of maternal deaths in the present era. There is a rising need to protect pregnant women from possible threats posed by abnormalities induced by changing physiological parameters. Pregnancy is a delicate stage and requires acute medical attention and care. Decision tree classification algorithms are popular and powerful methods most suitable for the medical diagnosis problems. The paper provides an insight into the standardization procedure and its impact on accuracy achieved by the C4.5 classifier to provide risk predictions during pregnancy. The aim of the paper is to highlight impact of parameter standardization on prediction accuracy achieved in present research. The performance of C4.5 decision tree classification algorithm selected for study in terms of accuracy obtained when applied on collected and standardized pregnancy data-set is also analyzed in the paper.