A Computational Analysis of Drug-Expanse Predictions from Biomaterials Using Ensemble Model for Healthcare Applications

The results of materials research laboratories' acquisition and evaluation of X-ray diffraction (XRD) data take a significant amount of time and include a lot of laborious and monotonous engineering through humans. To put this into perspective, a synopsis of the process of material synthesis that culminates in an investigation into the composition of the material can take several days to complete. This article explores an alternative approach that employs supervised learning algorithms based on ensemble-stacked neural network techniques. The method involves predicting the behavior of biomaterial granules incorporated with calcium phosphates (CaPs) as a matrix for carrying the anticancer drug carboplatin. The high-vacuum method is utilized for loading, and a set of five compositions is examined: hydroxyapatite (HA),
$\beta$
- tricalciumphosphate(
$\beta$
- TCP), biphasicHAp(60%
$\beta$
- TCP, 40%HA), -
$\beta$
TC-
$P$
/MgOnanocomposite, and
$\beta$
TCP/SiO2. Additionally, the study investigates the deposition of the carboplatin drug on the CaPs' surface at concentrations of 50, 60, and 70 mg/g, applying various machine-learning techniques for analysis. The deep neural network model successfully predicted the delivery release of different NPs with different amounts of phosphorous and calcium in the drug, and it did better than the stacked neural network (SNN) model ways. The determination of the results with the dataset for the following drug release with different compositions was around 97%. This study presents a quantitative approach that can improve the prediction of the drug release efficacy of nanomedicine in a manner that is both accurate and efficient.