A Literature Review of the Dimensionality Reduction Techniques for the Healthcare Domain

Abstract

The rapid advancement of healthcare technology has led to an explosion of multi-modal data, including electronic health records, medical imaging, genomics, and wearable device outputs. This diverse data landscape poses significant challenges in terms of dimensionality reduction, which is essential for effective analysis and interpretation. This literature review explores various multi-modal data fusion techniques aimed at enhancing dimensionality reduction in healthcare analytics. We categorize the existing approaches into three main frameworks: feature-level fusion, decision-level fusion, and hybrid methods, each exhibiting unique strengths and limitations. The review critically evaluates recent studies that leverage machine learning algorithms, deep learning architectures, and statistical methods for integrating multi-modal data. By synthesizing findings from various domains, we highlight the impact of dimensionality reduction on predictive modeling, disease diagnosis, and personalized treatment strategies. Furthermore, we discuss the challenges and future directions in the field, emphasizing the need for robust methodologies that ensure data integrity and interpretability while maintaining patient privacy. This review aims to provide a comprehensive understanding of current trends and advancements in multi-modal data fusion techniques, offering insights for researchers and practitioners in the realm of healthcare analytics.