Regression Analysis of Heat Transfer Induced by Bioconvection in Radiative Magnetohydrodynamic Blood Flow With Chemical Reaction, Soret and Dufour

ABSTRACT This research investigates how nanoparticles affect the flow of blood through biological tissues that are porous. The flow is described as a Casson nanofluid with bioconvection effects. It is carefully looked at how magnetic fields, chemical reactions, and thermophysical factors all affect the movement of heat and mass. Multiple linear regression (MLR) models were created to accurately predict important transport quantities, even when the interactions are complicated and not linear. When it came to accuracy, the Nn model got the best results ( R ² = 0.9808 for training and 0.9794 for testing; RMSE < 0.038; MAE < 0.030). The Sh and Cf/Nu models also did very well, with R ² values above 0.83. The findings show that thermophoresis and Brownian motion make temperature and heat flow much faster, while Dufour–Soret effects and chemical reaction effects make solute transport faster. Combining MLR with computational fluid dynamics is a cheaper and faster way to do things than using traditional numerical methods. It lets you better control thermal and concentration processes that are important for magnetic therapy, biomedical heat management, and targeted drug delivery.