# Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
## 4 Discussion  
CBSDG is a synthetic data approach that is unique in providing tractable and interpretable privacy-preservation built into the model. Therefore, it is worthwhile to study its practical potential. In this article, a published blood-transfusion data analysis was replicated with synthetic data to study the practical value of CBSDG. The analyses performed complement the previously published experiments in [23] by considering real data with features on which the method had not yet been evaluated. Blood-transfusion records were synthesized using data from the Dutch national blood bank. Prediction of Hb of blood-transfusion donors by [PERSON] et al. (2022) [26] were replicated with the synthetic data. CBSDG produced similar univariate distributions, including multimodal continuous variables and unbalanced binary data. To evaluate the similarity of variable relations, prediction of Hb of blood-transfusion donors by [PERSON] et al. (2022) [26] were replicated with the synthetic data. The predictions were largely identical to the results with the original data, and precision and recall scores were highly similar as well. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictor Variable** & \begin{tabular}{c} **Privacy** \\ **Measure** \\ \end{tabular} & \
\begin{tabular}{c} **MSPN on Training Donors** \\ \end{tabular} &
\begin{tabular}{c} **MSPN on Test Donors** \\ \end{tabular} \\ \hline Time (hours) & ED & 3.98 & 100 & 4.34 & 100 \\ Age (years) & ED & 15.38 & 100 & 17.33 & 100 \\ Month & ED & 4.11 & 100 & 4.43 & 100 \\ Last Ferritin & ED & 71.07 & 100 & 75.89 & 100 \\ Time since last ferritin measurement (days) & ED & 152.68 & 100 & 189.91 & 100 \\ Previous Hb & ED & 0.69 & 100 & 0.78 & 100 \\ Time since last Hb measurement (days) & ED & 62.68 & 100 & 91.06 & 100 \\ Deferral Status (0 = deferred, 1 = donated) & PoAC & 0.21 & 21 & 0.22 & 22 \\ Sex (0 = female, 1 = male) & PoAC & 0.43 & 43 & 0.47 & 47 \\ \hline \hline \end{tabular} Expected deviation (ED) is used for ordered variables and Proportion of Alternatives Considered (PoAC) for unordered variables. A value of 0 implies a potential privacy breach.  
\end{table} 
Table 1: Privacy results for the model with which the synthetic data were generated (the MSPN), tested for 8000 training donors and 4000 test donors. Furthermore, visualization of variable importance depicted that these results with synthetic and original data were alike, also when differentiated between sexes. This indicates that both the predictions themselves and the reasoning for these predictions were comparable. Additionally, variation of precision and recall was low, indicating that the approach consistently performed well across different synthetic data sets. With respect to privacy, an attacker is not expected to be able to infer sensitive values with 100% accuracy, for values on a continuous scale. For categorical values, the probability of correct inference was not larger for donors who were used to generate the synthetic data, than for donors who were not part of the synthetic-data-generation process. The synthetic data would have led to predominantly the same deferral predictions with minimal to no loss in accuracy. Furthermore, with the synthetic data, similar insights could have been acquired regarding associations between Hb deferral and specific donor characteristics, also when differentiating between male and female donors. This shows that the synthetic data sets have potential to substitute sensitive data in machine learning analyses, which are increasingly being used for blood-transfusion research [32]. Compared to other synthetic data generators, CBSDG has several advantages. First, the model (a restricted MSPN) requires little knowledge from users and no parametric distributions are assumed (as opposed to e.g., [33]), nor were (hyper)parameter settings required, other than the number of clusters. In addition, the way in which clusters yield privacy is explainable, which is an increasingly important factor in the development of privacy-enhancing technologies [34].