Studying the Hidden Relationships in Mixed Data Via Principal Component Analysis with Application in Traumatic Brain Injury Data

Abstract

The analysis of complex biomedical datasets often involves a mix of numerical and categorical variables, posing challenges for traditional statistical techniques. To address this limitation, this study proposes the use of Principal Component Analysis for Mixed Data (PCAmix). PCAmix is a powerful technique that can effectively reduce the dimensionality of complex datasets while preserving the most important information. By combining the strengths of Principal Component Analysis (PCA) and Multiple Correspondence Analysis (MCA), PCAmix can handle both numerical and categorical variables simultaneously. This flexibility allows for a more comprehensive analysis of complex datasets, particularly in the field of biomedical research. In this study, we applied PCAmix to a real-world biomedical dataset to investigate the intricate relationship between brain injury, functional outcomes, and genetic factors. The results we obtained illustrate not only the efficacy of PCAmix but also its practical uses in recognizing underlying frameworks, streamlining analysis by minimizing the number of variables while retaining essential information, creating predictive models to anticipate patient results, including functional recovery and cognitive deficits, and categorizing patients according to shared traits to facilitate tailored treatment approaches. Through the application of PCAmix, we gained a deeper understanding of the complex interplay between these factors and identified potential biomarkers for predicting patient outcomes. These findings have significant implications for the development of more effective diagnostic tools, prognostic models, and therapeutic interventions for traumatic brain injury. Ultimately, researchers can contribute to advancements in healthcare and medicine by unlocking valuable insights from complex biomedical data by leveraging the potential of PCAmix.