## 4 Discussion  
The first portion of the results dealt with the statistical assessment of the model personalization accuracy. This assessment was substantiated by the mean value and the covariance matrix of the parameter estimate residuals as given in Table 2. Since the mean value of the residuals is relatively close to zero, it can be claimed that the personalization does not produce statistically biased estimates of the parameters. Another important result to highlight is that the diagonal entries of the covariance matrix of the estimate residuals satisfy the variance condition given by (25) with a large margin. This proves that personalization significantly lowers the uncertainties of the model parameters. For better interpretation, the relative interval uncertainty of the original parameter vector \(\frac{\pm\Delta\theta}{\theta}\) obtained according to (12) and the relative interval uncertainty of the estimated (personalized) parameter vector \(\frac{\pm\Delta\theta}{\theta}\) obtained according to (29) were visualized in Figure 3, demonstrating the shrinking of the uncertainty interval after personalization. Figure 10: Simulation of the suboptimal chemotherapy designed based on the nominal model. It also implies that the trained MLP NN captures and sufficiently generalizes the relationships between the variations in the parameters, the initial state, dose size, and the state responses to a leading dose of chemotherapy. Besides comparing the estimated parameters themselves, the personalized models were validated by comparing their responses with the reference state responses used for personalization. This can be seen as a validation of the personalized model in the time domain. Such a comparison is documented in Figures 6 and 8. By studying these figures, it can be concluded that the personalized models provided an almost perfect match of the state responses. To confirm this observation, the root mean squared error was computed by comparing state responses between the estimated and reference models for each subject in the validation dataset, separately for each state variable \(N(t)\), \(L(t)\), \(T(t)\) as documented in Table 3. These results revealed that for the sample time \(T_{s}=1/4\) the metrics \(\text{RMSE}_{N}=2.2891\times 10^{4}\), \(\text{RMSE}_{L}=4.4498\times 10^{4}\), \(\text{RMSE}_{T}=5.4686\times 10^{5}\) are sufficient, especially if taken relative to the typical magnitudes of the state responses, which have average values \(\overline{N}(t)=2.4965\times 10^{5}\), \(\overline{L}(t)=1.4522\times 10^{6}\) and \(\overline{T}(t)=3.9565\times 10^{6}\). This indicates that the mean error of the state responses provided by the personalized model is only a few percent \(\frac{\text{RMSE}_{N}}{N(t)}=0.0922\), \(\frac{\text{RMSE}_{L}}{\overline{L}(t)}=0.0307\), \(\frac{\text{RMSE}_{L}}{\overline{T}(t)}=0.1382\) and that the best accuracy was achieved for \(L(t)\) and the worst for \(T(t)\). In contrast, increasing the sample time to \(T_{s}=1\) day resulted in higher \(\text{RMSE}_{N}\), \(\text{RMSE}_{L}\) and \(\text{RMSE}_{T}\) indicating slightly deteriorated, yet acceptable accuracy of the state responses prediction and good overall model validity under reduced sampling frequency. Increasing the sampling interval to \(T_{s}=1\) day also led to marginally higher variances of the residuals of the estimated parameters and broader confidence intervals in Figure 3. Nonetheless, this analysis remains crucial, as frequent data collection may pose practical challenges in clinical settings. For the sake of comparison with other relevant parameter identification approaches, the conventional least squares minimization of the model response error with respect to the reference state responses was evaluated for the validation dataset and the obtained results were quantified by statistical metrics as summarized in Tables 2 and 3.