# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
## 4 Discussion
Using the results of the \(\chi^{2}\) analysis, reduced-feature-set datasets were constructed using the top 5, 6, 7, 8, and 9 features. The minimum feature set of five was determined as the threshold in the \(\chi^{2}\) results where the subsequent features did not have a large difference in rank score from the previous features in Figure 2. These reduced-feature datasets were used to train ANNs and SVMs, with the resulting metrics tabulated in Table 3. As expected, the models trained on reduced features had higher performance than the full-feature datasets. Four out of five models had an AUC > 0.8 and three of the five also had improved MCCs > 0.3. The ROC curves and truth tables of these selected models are provided in Figure 4, with their performance metrics presented in Table 3 alongside those of the other models. The SVM versions of the reduced-feature models did not see such a boost in performance as observed with the ANN models. In most cases, the performance was reduced compared to the full-feature model. This result was likely due to the reduced features the SVM had available to train the model. While ANNs act as if they have a 'logic' to their decisions, SVMs create support vectors from the available training data to separate the outcomes in a higher dimensional space. Reducing the feature size reduces the available variables for the SVM to create these support vectors, which is likely why the reduced-feature SVM models lost performance compared to the ANN models. Ultimately, the goal of these models is to inform researchers and clinicians of the potential of a proposed set of parameters before spending resources to investigate them directly. However, as shown in the collected dataset, the field at large operates on a wide range of species and tissue types and uses a variety of plasmid DNA, depending on the application. The wide variability in application and subject tissue presents a limitation in model generalizability. While these models would perform well given the tissues, species,or electrodes that the models were trained on, the introduction of a tissue or electrode unknown to the model would likely not provide similar results to those presented here. A solution to this generalization problem is to expand the electrode, tissue, and species variables beyond a simple label. FEM simulations of electric field strengths between the electrodes would alleviate part of this unknown by allowing for the entry of electrode and tissue parameters directly into both the FEM and machine learning models, effectively specifying electrodes by their field distributions and tissues by their electrical parameters rather than by labels. This one step would eliminate a large portion of the limitation in the generalizability of these models, but would take a concerted effort by the community at large to achieve. Finally, globally, there is a notable limitation of this study due to inherent variabilities from lab to lab and even from operators within labs in how experiments are designed and carried out, how data are reported, and how field strengths are determined. The variability of GET test equipment and the sensitivity of the analyses are inherent in such an analysis as there is a lack of standardization in the hardware, software, test methods, and reporting, as we noted in reviewing 19 different papers for this model. Field strengths in particular are often reported in a simplified manner using Equation (1). This can be an oversimplification, as shown in Figure 1, where the field strengths and thus voltage distributions are anything but uniform. Furthermore, it is not typical for labs to report failed experiments unless they are relevant in the context of reporting successful treatment, such as in the case of a known negative control. This leads to lopsided datasets that are full of successes, with very few failures.