## 3 Results and Discussion
### Parametric Predicative Modeling  
The study by [PERSON] et al. [56] demonstrated that feature importance analysis in random forests can reveal dominant predictors of nanoparticle cytotoxicity, such as surface chemistry and dissolution rate. Another researcher [37] showed that the Scikit-learn library, a widely recognized framework for machine learning applications in scientific research, can be utilized. Recent advancements in machine learning have demonstrated random forest regression utility in biomaterials research, particularly for predicting cytotoxicity based on material composition and physicochemical properties [38]. Noteworthy, the previous study [56; 57; 58; 59; 60] explored the use of machine learning for biomaterial analysis, emphasizing the importance of feature selection and model interpretability in predictingbiological responses. The model architecture was carefully optimized through extensive cross-validation and hyperparameter tuning according to other researchers' data [40]. In our study, SHAP analysis indicates that MgO concentration plays a primary role in influencing cell viability, reinforcing the importance of material composition in biomaterial safety assessments (Figures 14-16). Feature engineering and data processing in the model incorporate eight critical features that characterize the biomaterial composites: PLA quantity (g), which represents the base polymer content; MgO presence, a binary indicator of MgO incorporation; MgO quantity (g), the precise amount of MgO; Au presence, a binary indicator of gold nanoparticle incorporation; Au quantity (mL), the volume of gold nanoparticle solution; porosity (%), the material porosity measurement; pH, the pH level after 21 days of incubation; and mass (mg), the sample mass after 3 days. To ensure optimal model performance, a standardization procedure using StandardScaler was implemented, normalizing the numerical features to a common scale. The model demonstrates noteworthy predictive capabilities, achieving a root mean square error (RMSE) of 8.99 (\(\pm\)7.22) through five-fold cross-validation. This performance metric indicates strong predictive power while acknowledging the inherent variability in biological systems. The mean absolute error of 3.51% suggests high practical utility, with predictions typically falling within an acceptable range for biomaterial development applications. Error distribution analysis reveals a minimum error of 0.31% (exceptional accuracy for some predictions), a maximum error of 7.90% (acceptable maximum deviation), a median absolute error of 3.73% (consistent with mean performance), and a standard deviation of error of 2.30% (indicating stable predictions). SHAP (SHapley Additive exPlanations) is a model-agnostic interpretability method based on cooperative game theory. It assigns each feature in a machine learning model a SHAP value, which represents its contribution to a specific prediction. By calculating the average marginal contribution of each feature across all possible combinations, SHAP provides a quantitative and consistent explanation of how  
Figure 15: Correlation between predicted and actual osteoblasts viability. input features influence the model's output, enabling a clearer understanding and trust in complex models like random forests. Through SHAP analysis, valuable insights into the relative importance of different material properties were gained. MgO quantity emerged as a primary determinant of cell viability [%], showing a strong negative correlation with toxicity levels, suggesting that careful control of MgO content could be used to modulate material biocompatibility. Gold nanoparticle concentration demonstrated significant influence, with higher concentrations generally associated with lower cytotoxicity levels, aligning with the existing literature on the biocompatibility of gold nanoparticles. pH and mass showed complex relationships with cytotoxicity, indicating potential interaction effects that warrant further investigation.