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Heart failure (HF) affects at least 26 million people worldwide, sopredicting adverse events in HF patients represents a major target of clinicaldata science. However, achieving large sample sizes sometimes represents achallenge due to difficulties in patient recruiting and long follow-up times,increasing the problem of missing data. To overcome the issue of a narrowdataset cardinality (in a clinical dataset, the cardinality is the number ofpatients in that dataset), population-enhancing algorithms are thereforecrucial. The aim of this study was to design a random shuffle method to enhancethe cardinality of an HF dataset while it is statistically legitimate, withoutthe need of specific hypotheses and regression models. The cardinalityenhancement was validated against an established random repeated-measuresmethod with regard to the correctness in predicting clinical conditions andendpoints. In particular, machine learning and regression models were employedto highlight the benefits of the enhanced datasets. The proposed random shufflemethod was able to enhance the HF dataset cardinality (711 patients beforedataset preprocessing) circa 10 times and circa 21 times when followed by arandom repeated-measures approach. We believe that the random shuffle methodcould be used in the cardiovascular field and in other data science problemswhen missing data and the narrow dataset cardinality represent an issue.

A random shuffle method to expand a narrow dataset and overcome the associated challenges in a clinical study: a heart failure cohort example