  Missing data is a common concern in health datasets, and its impact on good
decision-making processes is well documented. Our study's contribution is a
methodology for tackling missing data problems using a combination of synthetic
dataset generation, missing data imputation and deep learning methods to
resolve missing data challenges. Specifically, we conducted a series of
experiments with these objectives; $a)$ generating a realistic synthetic
dataset, $b)$ simulating data missingness, $c)$ recovering the missing data,
and $d)$ analyzing imputation performance. Our methodology used a gaussian
mixture model whose parameters were learned from a cleaned subset of a real
demographic and health dataset to generate the synthetic data. We simulated
various missingness degrees ranging from $10 \%$, $20 \%$, $30 \%$, and $40\%$
under the missing completely at random scheme MCAR. We used an integrated
performance analysis framework involving clustering, classification and direct
imputation analysis. Our results show that models trained on synthetic and
imputed datasets could make predictions with an accuracy of $83 \%$ and $80 \%$
on $a) $ an unseen real dataset and $b)$ an unseen reserved synthetic test
dataset, respectively. Moreover, the models that used the DAE method for
imputed yielded the lowest log loss an indication of good performance, even
though the accuracy measures were slightly lower. In conclusion, our work
demonstrates that using our methodology, one can reverse engineer a solution to
resolve missingness on an unseen dataset with missingness. Moreover, though we
used a health dataset, our methodology can be utilized in other contexts.
