## 4 Discussion and Outlook  
In this study, we evaluated three different classification models for breast cancer detection using EIT images. We found that all three models were able to produce meaningful results for data outside the trained distributions.  
RFs were a robust choice under varying noise conditions, as they showed exceptional resistance to variations in SNR. On the other hand, SVMs showed resilience to changes in tumor size, indicating their potential for the detection of tumors of different sizes.  
However, it is important to note that when the SNR drops too low, all models become unreliable in their predictions. This underlines the fact that for accurate and reliable predictions, a certain level of SNR must be maintained.  
Solely based on score, SVMs would be the choice for the prediction of breast cancer. They consistently achieved the best F1 scores in all tests, indicating their superior performance in terms of precision and recall. Furthermore, SVMs performed well even in scenarios involving small tumors, which is critical for detecting breast cancer at an early stage.  
Furthermore, when considering real-world applications, the adoption of RFs may be favored due to their enhanced interpretability, allowing clinicians to better understand the model's decisions and incorporate their own expertise. RFs provide feature importance analysis and transparency by examining decision boundaries, which are critical for promoting trust and informed decision-making in the medical domain. However, further research is needed to comprehensively evaluate the interpretability of RFs compared to SVMs and ANNs in different clinical contexts.  
Despite these promising results, our study has some limitations. One such limitation is the fixed breast size used in our simulations. In reality, the size of the breast varies considerably from person to person, and this variation could potentially affect the potentials at the electrodes. Future research should consider including differently shaped breasts in the simulations to account for this variability.  
Figure 14: Performance of the RF for a tumor radius range of 6 mm to 13 mm and an SNR of 30, while testing was done different radii and SNR levels.  
Figure 15: Performance of the ANN for a tumor radius range of 6 mm to 13 mm and an SNR of 30, while testing was done different radii and SNR levels.  
Another area for improvement is the pre-processing of the voltages. Our study suggests that reducing dimensionality can improve the performance of prediction models by making them more robust and requiring fewer parameters for training. This may be an important consideration for future studies aimed at optimizing the performance of models used to interpret EIT images.  
As of now, an EIT-based setup is behind in minimal size compared to classical screening techniques like mammography [28].  
In conclusion, our study provides valuable insights into the use of classification models for the interpretation of EIT images in the detection of breast cancer. Although further research is needed to address the identified limitations, our findings represent a step forward in improving early detection and ultimately survival rates for breast cancer patients.  
Conceptualization, J.R. and S.L. ([PERSON]); methodology, J.R.; software, N.B.; validation, J.R. and S.L. ([PERSON]); formal analysis, J.R.; investigation, J.R., N.B. and S.L. ([PERSON]); resources, S.L. ([PERSON]); data curation, N.B.; writing--original draft preparation, J.R.; writing--review and editing, J.R.; visualization, N.B.; supervision, J.R.; project administration, S.L. ([PERSON]); funding acquisition, S.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge financial support provided by the project InDiThera (13 GW0361E) through the Federal Ministry of Education and Research.  
Not applicable.  
The data itself can be simulated with the explained simulation strategy.  
The authors declare no conflict of interest.