  Missing values exist in nearly all clinical studies because data for a
variable or question are not collected or not available. Inadequate handling of
missing values can lead to biased results and loss of statistical power in
analysis. Existing models usually do not consider privacy concerns or do not
utilise the inherent correlations across multiple features to impute the
missing values. In healthcare applications, we are usually confronted with high
dimensional and sometimes small sample size datasets that need more effective
augmentation or imputation techniques. Besides, imputation and augmentation
processes are traditionally conducted individually. However, imputing missing
values and augmenting data can significantly improve generalisation and avoid
bias in machine learning models. A Bayesian approach to impute missing values
and creating augmented samples in high dimensional healthcare data is proposed
in this work. We propose folded Hamiltonian Monte Carlo (F-HMC) with Bayesian
inference as a more practical approach to process the cross-dimensional
relations by applying a random walk and Hamiltonian dynamics to adapt posterior
distribution and generate large-scale samples. The proposed method is applied
to a cancer symptom assessment dataset and confirmed to enrich the quality of
data in precision, accuracy, recall, F1 score, and propensity metric.
