Improving prediction of maternal health risks using PCA features and TreeNet model

Abstract

Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother’s health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women’s safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches.

Keywords

• Maternal health risk detection
• Feature engineering
• Healthcare
• PCA features
• Ensemble learning