Several indices exist to classify Congestive Heart Failure (CHF) patients' propensity for early mortality; however, they are primarily based on limited data and are not intuitive to use at the point of care. We investigate a novel, data-driven, risk assessment and visualization approach to investigate mortality prediction of CHF patients using data retrieved from an intensively digitized hospital's data repository. Combining well-known, computationally efficient, dimensionality reduction (DR) methods with 2-d information visualization, the method classifies and visualizes CHF patients into high and low risk groups, contextualized by the factors driving their classification. The DR method performed similar to logistic regression (LR), but visualized the classification and its significant factors at the population level, individual level and the potential impact of interventions for an individual patient. These are encouraging results in favor of the proposed visualization approach, and contributes to the current focus on advancing patient care via large-scale visual analytics.