  Plasma is defined as the fourth state of matter and non-thermal plasma can be
produced at atmospheric pressure under a high electrical field. The strong and
broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now
well known. The proven applicability of machine learning (ML) in the medical
field is encouraging for its application in the field of plasma medicine as
well. Thus, ML applications on PALs could present a new perspective to better
understand the influences of various parameters on their antimicrobial effects.
In this paper, comparative supervised ML models are presented by using
previously obtained data to qualitatively predict the in vitro antimicrobial
activity of PALs. A literature search was performed and data is collected from
33 relevant articles. After the required preprocessing steps, two supervised ML
methods, namely classification, and regression are applied to data to obtain
microbial inactivation (MI) predictions. For classification, MI is labeled in
four categories and for regression, MI is used as a continuous variable. Two
different robust cross-validation strategies are conducted for classification
and regression models to evaluate the proposed method; repeated stratified
k-fold cross-validation and k-fold cross-validation, respectively. We also
investigate the effect of different features on models. The results
demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC)
and Random Forest Regressor (oRFR) provided better results than other models
for the classification and regression, respectively. Finally, the best test
accuracy of 82.68% for oRFC and R2 of 0.75 for the oRFR are obtained. ML
techniques could contribute to a better understanding of plasma parameters that
have a dominant role in the desired antimicrobial effect. Furthermore, such
findings may contribute to the definition of a plasma dose in the future.
