## 3 Results and Discussion
### Parametric Predicative Modeling
Porosity exhibited a moderate negative correlation with prediction errors (\(-0.313\)), suggesting that higher porosity materials may be more predictable in terms of their cytotoxic behavior. Figure 16: SHAP summary plot and value. While the current model demonstrates strong predictive capabilities, several limitations and opportunities for improvement exist. The dataset size, currently limited to a training set of 10 samples, constrains the model's ability to capture more complex patterns. Extreme value prediction remains a challenge, as the model shows slightly reduced accuracy for very high cytotoxicity values, suggesting room for improvement in handling extreme cases. Additionally, the current implementation could benefit from more sophisticated uncertainty estimation methods. Future research directions include data enhancement by expanding the training dataset with more diverse samples, focusing on collecting data for extreme cytotoxicity cases, and investigating additional material properties that might influence cytotoxicity. Model improvements can be pursued by exploring advanced ensemble methods combining multiple algorithm types and incorporating genetic algorithms from the Mealpy library, as their accuracy appears promising based on initial tests. The authors of the article [41] employed evolutionary algorithm techniques, specifically genetic algorithms (GAs), for model and band selection in hyperspectral image classification. This approach demonstrates the significant potential of such algorithms in the medical and diagnostic sectors, particularly in optimizing classifiers for forensic-inspired hyperspectral data analysis. Future directions should also include the development of specialized models for different cytotoxicity ranges. Further improvements in feature engineering should involve investigating interaction terms between features, exploring non-linear transformations of input variables, and considering time-dependent features for long-term toxicity prediction. These advancements will enhance the predictive power and robustness of the model in biomaterial development applications. The scatter plot highlights the correlation between actual and predicted survivability percentages, with most data points closely following the ideal prediction line. However, deviations, particularly at higher survivability values, suggest potential limitations in the model's generalization. Given the small dataset size (only 10 samples), the model likely struggles to capture complex patterns, leading to increased variance and reduced predictive accuracy in certain cases. The observed discrepancies indicate that while the random forest regression model effectively captures the overall trend, a larger and more diverse dataset would be necessary to improve prediction reliability and reduce errors in extreme cases. The SHAP summary plot illustrates the impact of each feature on the model's output, with MgO quantity (MgO_quant) having the strongest effect, where higher values (blue) reduce survivability, likely due to cytotoxic effects (Figure 15). Gold nanoparticle concentration (Au_quant) also significantly influences predictions, with high values (blue) generally increasing survivability. pH and mass exhibit moderate effects, suggesting their role in biocompatibility and material stability. Features such as binary presence indicators (MgO, Au), porosity, and PLA quantity contribute less, reinforcing that the amount of MgO and Au is more critical than their mere presence. These insights suggest a need to optimize MgO levels while leveraging Au to improve biomaterial performance.