Microneedles (MNs)
allow for biological fluid sampling and drug
delivery toward the development of minimally invasive diagnostics
and treatment in medicine. MNs have been fabricated based on empirical
data such as mechanical testing, and their physical parameters have
been optimized through the trial-and-error method. While these methods
showed adequate results, the performance of MNs can be enhanced by
analyzing a large data set of parameters and their respective performance
using artificial intelligence. In this study, finite element methods
(FEMs) and machine learning (ML) models were integrated to determine
the optimal physical parameters for a MN design in order to maximize
the amount of collected fluid. The fluid behavior in a MN patch is
simulated with several different physical and geometrical parameters
using FEM, and the resulting data set is used as the input for ML
algorithms including multiple linear regression, random forest regression,
support vector regression, and neural networks. Decision tree regression
(DTR) yielded the best prediction of optimal parameters. ML modeling
methods can be utilized to optimize the geometrical design parameters
of MNs in wearable devices for application in point-of-care diagnostics
and targeted drug delivery.