Abstract
Missing values exist in nearly all clinical studies because data for a variable or
question are not collected or not available. Imputing missing values and augmenting
data can significantly improve generalisation and avoid bias in machine learning
models. We propose a Hybrid Bayesian inference using Hamiltonian Monte Carlo
(F-HMC) as a more practical approach to process 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 cancer
symptom assessment, and MNIST datasets confirmed to enrich data quality in
precision, accuracy, recall, F1-score, and propensity metric.