Prediction based health monitoring in pregnant women

Gestation or pregnancy a stage where women undergo several physiological changes, sometimes inducing complications turning severe and initiating instances leading to death of both mother and fetus. Pregnant women must thus be protected from complications arising during gestation period. Several classification algorithms are successfully implemented in several fields. Decision Tree Classification Method is one efficient method best suitable for medical diagnosis. A popular algorithm C4.5 Decision Tree classification algorithm is appropriate for classifying the pregnancy data. The algorithm constructs a learning model from the training data and later risks in pregnancy are predicted for unseen pregnancy data. The main aim of this paper is to optimise performance of C4.5 classification algorithm by applying on standardized and appropriate format of data. The paper highlights the effective performance achieved by C4.5 classifier in accurately predicting risk levels during pregnancy from the collected, standardized and transformed data efficiently.