Analysis of Nanodrug Delivery in Blood Flowing through Blood Vessels Using Machine Learning Models

This study provides a framework to strategize localized efficient drug delivery in second-order blood flowing through porous blood vessels using machine learning algorithms. With the assumption of long blood vessels, the flow-governing equation, the Navier–Stokes equation, is reduced to a simpler model which is consistent with the lubrication theory. We solved this equation analytically with slip conditions and obtained the analytical expression of the velocity profile for the Newtonian model. We modelled the concentration of nanodrugs with an advection diffusion equation to analyze the effect of concentration on the localized disease. The particle concentration at the blood vessel wall was evaluated using the finite-difference method. To analyze the particle concentration, we implemented machine learning algorithms including Gradient Boost, XG Boost, Regression Tree, MLP Regressor, and CatBoost Regressor. Our conclusion predicts the optimum machine learning algorithm for transferring the delivery of the nanoparticle drug.