  Heart failure (HF) affects at least 26 million people worldwide, so
predicting adverse events in HF patients represents a major target of clinical
data science. However, achieving large sample sizes sometimes represents a
challenge due to difficulties in patient recruiting and long follow-up times,
increasing the problem of missing data. To overcome the issue of a narrow
dataset cardinality (in a clinical dataset, the cardinality is the number of
patients in that dataset), population-enhancing algorithms are therefore
crucial. The aim of this study was to design a random shuffle method to enhance
the cardinality of an HF dataset while it is statistically legitimate, without
the need of specific hypotheses and regression models. The cardinality
enhancement was validated against an established random repeated-measures
method with regard to the correctness in predicting clinical conditions and
endpoints. In particular, machine learning and regression models were employed
to highlight the benefits of the enhanced datasets. The proposed random shuffle
method was able to enhance the HF dataset cardinality (711 patients before
dataset preprocessing) circa 10 times and circa 21 times when followed by a
random repeated-measures approach. We believe that the random shuffle method
could be used in the cardiovascular field and in other data science problems
when missing data and the narrow dataset cardinality represent an issue.
