PCA-Based Dimensionality Reduction for IoMT Prenatal Data Analytics

The rapid growth of the Internet of Medical Things (IoMT) has led to an abundance of data generated by medical devices and sensors. However, the high dimensionality of this data poses challenges in terms of storage, processing, and analysis. In this paper, we propose a novel approach to enhance the efficiency of IoMT devices through Principal Component Analysis (PCA)-based dimensionality reduction. By applying PCA, we aim to capture the most relevant information from the high dimensional IoMT data and represent it in a lower-dimensional space. This reduction in dimensionality not only reduces the computational burden but also facilitates data visualization and interpretation. We demonstrate the effectiveness of our proposed approach through experiments conducted on areal-world IoMT dataset. The results show that PCA-based dimensionality reduction significantly improves the efficiency of IoMT devices while preserving the essential information required for accurate analysis and decision-making.