Predictive Modeling for Fetal Health: A Comparative Study of PCA, LDA, and KPCA for Dimensionality Reduction

Pregnancy complications significantly impact maternal and fetal health, requiring accurate and timely diagnostic methods for life-saving interventions. Traditional manual analysis of cardiotocography (CTG) tests, a commonly used technique for fetal health monitoring, is labor-intensive and prone to variability. This study addresses this issue by applying dimensionality reduction techniques—Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Kernel Principal Component Analysis (KPCA)—to improve the performance of machine learning (ML) models in predicting fetal health using CTG data. Using a dataset of 2,126 records, our goal was to reduce the feature space while preserving essential information, resulting in improved classification accuracy. The results show that PCA with XGBoost achieved 98% accuracy, LDA with K-nearest Neighbors achieved 91%, and KPCA with XGBoost achieved 97%. These findings highlight the importance of dimensionality reduction and feature selection in developing robust ML models for fetal health assessment, emphasizing their potential impact on improving clinical diagnostics and medical decision-making.