Application of Principal Component Analysis (PCA) to Uncover Clinical Patterns in Patients with Heart Failure
DOI:
https://doi.org/10.24252/msa.v14i2.69979Keywords:
Principal Component Analysis, heart failure, clinical data, dimensionality reduction, multivariate analysisAbstract
Heart failure is a complex clinical syndrome characterized by numerous interrelated clinical variables, making the identification of essential disease patterns increasingly challenging. Advances in medical measurement technologies have generated high-dimensional clinical datasets that require efficient analytical approaches to extract meaningful information for clinical decision-making. This study aims to apply Principal Component Analysis (PCA) to reduce the dimensionality of clinical variables while preserving the underlying information structure. The analysis was conducted using the Heart Failure Clinical Records dataset from the UCI Machine Learning Repository, comprising 299 patients with 13 clinical variables. The results showed that the first three principal components accounted for 52.57% of the total data variance. The first principal component (PC1) contributed the largest proportion of explained variance and was primarily associated with serum creatinine, follow-up time, and serum sodium levels, all of which were closely related to patient mortality. Furthermore, biplot visualization demonstrated the ability of PCA to distinguish patient groups according to their clinical outcomes. These findings highlight the effectiveness of PCA as an exploratory statistical tool for summarizing complex clinical data, identifying underlying clinical patterns, and supporting evidence-based medical decision-making.
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