  Because of its strong predictive skills, deep learning has emerged as an
essential tool in many industries, including healthcare. Traditional deep
learning models, on the other hand, frequently lack interpretability and omit
to take prediction uncertainty into account two crucial components of clinical
decision making. In order to produce explainable and uncertainty aware
predictions, this study presents a novel framework called Bayesian Kolmogorov
Arnold Networks (BKANs), which combines the expressive capacity of Kolmogorov
Arnold Networks with Bayesian inference. We employ BKANs on two medical
datasets, which are widely used benchmarks for assessing machine learning
models in medical diagnostics: the Pima Indians Diabetes dataset and the
Cleveland Heart Disease dataset. Our method provides useful insights into
prediction confidence and decision boundaries and outperforms traditional deep
learning models in terms of prediction accuracy. Moreover, BKANs' capacity to
represent aleatoric and epistemic uncertainty guarantees doctors receive more
solid and trustworthy decision support. Our Bayesian strategy improves the
interpretability of the model and considerably minimises overfitting, which is
important for tiny and imbalanced medical datasets, according to experimental
results. We present possible expansions to further use BKANs in more
complicated multimodal datasets and address the significance of these
discoveries for future research in building reliable AI systems for healthcare.
This work paves the way for a new paradigm in deep learning model deployment in
vital sectors where transparency and reliability are crucial.
