  Cardiovascular disease (CVD) is a class of diseases that involve the heart or
blood vessels and according to World Health Organization is the leading cause
of death worldwide. EHR data regarding this case, as well as medical cases in
general, contain missing values very frequently. The percentage of missingness
may vary and is linked with instrument errors, manual data entry procedures,
etc. Even though the missing rate is usually significant, in many cases the
missing value imputation part is handled poorly either with case-deletion or
with simple statistical approaches such as mode and median imputation. These
methods are known to introduce significant bias, since they do not account for
the relationships between the dataset's variables. Within the medical
framework, many datasets consist of lab tests or patient medical tests, where
these relationships are present and strong. To address these limitations, in
this paper we test and modify state-of-the-art missing value imputation methods
based on Generative Adversarial Networks (GANs) and Autoencoders. The
evaluation is accomplished for both the tasks of data imputation and
post-imputation prediction. Regarding the imputation task, we achieve
improvements of 0.20, 7.00% in normalised Root Mean Squared Error (RMSE) and
Area Under the Receiver Operating Characteristic Curve (AUROC) respectively. In
terms of the post-imputation prediction task, our models outperform the
standard approaches by 2.50% in F1-score.
