Advanced hybrid numerical-machine learning computational study on fluid flow modeling in magnetic nanocarriers for targeted drug delivery

Development of comprehensive models for simulation of blood flow containing magnetic nanoparticles is of fundamental importance due to its application in medical science and cancer treatment. Owing to the complexity of the process, advanced hybrid models are preferred over conventional models. The focus of this study is on the application of machine learning models for predicting velocity in a given dataset to study the fluid flow in vessel containing magnetic nanocarrier for targeted cancer therapy. The numerical model was performed to simulate the blood flow through vessel, while the output data was used in developing machine learning models. The dataset is utilized to train three distinct models: Random Forest, Extra Tree, and AdaBoost Decision Tree. The hyper-parameter optimization is carried out via the Whale Optimization Algorithm (WOA) to improve the models' predictive capabilities. The dataset encompasses more than 17 thousand rows of data. The findings demonstrate the effectiveness of the suggested methodology, as evidenced by the impressive performance metrics exhibited by each model. The Random Forest model achieved a high R-squared value of 0.98768, indicating a robust fit to the data. The Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for Random Forest were recorded at 6.1223E-04 and 6.09246, respectively. Similarly, the Extra Tree model exhibited superior predictive accuracy with a score of 0.99151 with R-squared metric, showcasing its skill in uncovering the fundamental patterns present in the dataset. The associated RMSE and MAPE for Extra Tree were notably low at 5.1682E-04 and 1.20955E-01, respectively. The AdaBoost Decision Tree model exhibited notable efficacy, as evidenced by its R-squared score of 0.96944, albeit marginally lower than the other models. The RMSE and MAPE for AdaBoost DT were reported at 9.1863E-04 and 8.06333, respectively.