# Machine Learning for Predicting Neutron Effective Dose
## 3 Results and Discussion  
Figure 2 shows a comparison of the XGB, GB, and EXTR models. These models used both numerical and categorical data (XGB-C, GB-C, and EXTR-C) to predict doses for the target voxel-based anthropomorphic phantom. Selected organ doses for the colon and red bone marrow (RBM) at different irradiation positions (AP, PA, and LAT) were compared with the ICRP-116 mesh-based and ICRP-110 voxel-based anthropomorphic reference calculated the organ doses. The results obtained when using predictive ML models (numerical and categorical) overlapped with each other; however, a deviation in the results of some models was observed. For red bone marrow (RBM) AP irradiation  
Figure 1: R\({}^{2}\) values for the categorical labeled data training set for the EXTR-C, XGB-C, and GB-C models, both actual (y) and predicted (y) units in pSv. The box plot in the lower right corner of the figure shows the results for the k-fold accuracy analysis with five cross-validations for all models on the training set for numerical data (EXTR, GB, XGB), categorical data (EXTR-C, GB-C, XGB-C), testing set for numerical data (EXTR-t, GB-t, XGB-t) and categorical data (EXTR-C-t, GB-C-t, XGB-C-t). conditions, the values for neutron energies lower than 1 eV had percentile differences that varied between 40% (for the EXTR, EXTR-C, and XGB-C models) and 87% (for GB, GB-C, and XGB). For higher neutron energies, the values varied between 3% and 27% for all of the models. The ML model predictions varied, and the EXTR-C and XGB-C models generally performed better than the other models; for example, for the colon LAT DCCs in Figure 2, the XGB model using numerical data presented signs of underestimation and overfitting. All ML models scored an average percentile difference of less than 9% with respect to ICRP-116 LAT DCCs. The models employed for fitting categorical data (XGB-C, GB-C, and EXTR-C) showed reasonable agreement with the reference values of ICRP-116 and ICRP-110, considering the variation in the physical characteristics of the organ (assigned features weight and density) for the target voxel phantom. Notably, the results of ICRP-116 and ICRP-110 represented the average of several Monte Carlo codes calculations with a variation of 15% [13; 14]. For effective dose calculations, categorical data for organ dose prediction were considered using three linear ML models (XGB-C, GB-C, and EXTR-C). The calculation methods described in ICRP-103 and ICRP-116 using a function for the neutron energy radiation weighting factor dependency were adopted. ICRP-103 recommends a radiation-sensitive assigned weighting factor for 14 organs (Table 1) with the remaining tissues segmented in the Saudi voxel anthropomorphic phantom. Figure 2: _Cont._  
Figure 3 shows the results of the neutron effective dose CC for the AP, PA, and LAT irradiation conditions compared with ICRP-116. The comparison results, obtained through dividing the ML model-predicted value by the ICRP-116 values, are shown in Figure 3 (right lower corner graph). The PA had the largest deviation, which was followed by the AP and LAT. The major contributors to the received dose in organs depend primarily on the position of the organ relative to the irradiation source and the mean position volume of the organ within the phantom. Therefore, differences can be expected when comparing with the agreed reference anthropomorphic phantom provided by the ICRP. Figure 3 shows the PA effective dose calculations for the FAX voxel-based phantom using the ICRP-103 radiation weighting factors [23] and the Zubal voxel-based phantom [24]. The deviation was larger than the Saudi voxel-based ML model-predicted neutron effective dose CC versus the ICRP reference values. The use of different neutron radiation weighting factors and different numbers of organs in the calculations may explain the larger deviation for the FAX voxel phantom and, to a lesser extent, Zubal voxel-based phantoms, compared to that in the current study.