# Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes
## 4 Discussion  
To date, GET optimization requires the extensive use of costly cell cultures and animals to refine ideal PEF conditions and associated parameters to maximize expression with minimal side effects. A typical new GET study will iterate on the variables presented in Table 2 to achieve a set of working conditions that achieve the desired outcome. These myriad parameters are typically informed only by lab history itself, with highly varied preferences between labs. A model that is informed by a large body of data from multiple labs has the potential to enable physical studies to start from a more advantageous state and reduce the need for costly materials and lab time. These models narrow down the need for parameters that need to be tested empirically in animals for specific tissues with specific electrode configurations. Ultimately, this would reduce the time to clinical translation and get these new parameters into the hands of practicing clinicians sooner. This work presents a first foray into developing such ML algorithms to inform GET working conditions to reduce the variability, cost, and complexity of studies and improve overall study results with higher fidelity GET optimization informed by a trained predictive model. Figure 5: ROC AUC values and curves for each of the top 4 ANN models showed similarly acceptable values when the ANN was allowed access to only the (**a**) top 6 features, (**b**) top 7 features, (**c**) top 8 features, and (**d**) top 9 features. As a first step, basic ANN and SVM models were trained on the dataset without any feature reduction, with the results presented in Table 3. While the SVM model performed better than the ANN model, both models performed poorly. The accuracies, sensitivities, and specificities for both models were all below 70% and the MCCs for both models were about or below the value expected for a coin-flip predictor. The Basic SVM model did have an 'acceptable' AUC of >0.8, but, given the poor performance in other metrics, this metric on its own was not considered acceptable. In the training of ANNs, there are a multitude of parameters to adjust that may increase the performance of the underlying model. The first of these is the regularization strength, \(\lambda\). This parameter can be adjusted by iterating the underlying model under different values of \(\lambda\) and choosing the model that reduces error during validation. The optimized model had better performance than the Basic ANN, as shown in Table 3, but still performed relatively poorly when compared to desired thresholds. These were not unexpected results, as most machine learning models require large datasets to begin to achieve higher levels of accuracy and performance. ANNs specifically scale the required dataset size based on the number of features used to describe each data point. So, the more variables that are used to describe the underlying data, the more data points are required for an ANN to approach usable metrics. To reduce the effect of the relatively small sample size, the number of features used to train the models was reduced using the results of a bootstrapped \(\chi^{2}\) test. The results of one iteration of this test are provided in Figure 2, which shows that voltage and pulse width were amongst the features that most strongly predicted the outcome. This outcome follows prevailing theories in the field, where labs will generally start by adjusting physical pulse parameters when attempting to optimize a procedure. However, due to variability in the data, a bootstrapped version of the test was completed, where the data were resampled 100 times and 95% confidence intervals were plotted for each of the ranks. These data are summarized in Figure 3. Even after bootstrapping, these results show that, in the available data, pulse and electrode design were the most impactful features when it came to successful outcomes, reinforcing the explainability of the underlying 'logic' of the machine learning models.