## 4 Discussion
Various ML models were employed,including SVMs, artificial neural networks (ANNs), and random forests (RFs), to predict these effects using datasets ranging from 79 to 754 patients. Some studies also explored genetic and radiomic markers. For instance, [PERSON] et al. [146] used gene ontology to link neurogenesis and ion transport to urinary functions, while [PERSON] et al. [147] examined MRI radiomic features to predict toxicity. ANN and logistic regression models were common approaches, as seen in [PERSON] et al. and [PERSON] et al. [148; 149], who found spatial dose distribution to be crucial for reducing GI toxicity. Other advanced methods included independent component analysis (ICA), as in [PERSON] et al. [150], and tensor decomposition by CP-DMA. Incorporating clinical, dosimetric, and radiomic data often enhanced predictive accuracy. [PERSON] et al. [151], for instance, combined MRI features to improve early rectal toxicity predictions. [PERSON] et al. [62] explored a variety of statistical learning strategies to predict urinary symptoms following RT in PCa, each with unique strengths for handling complex datasets. Logistic regression, a widely used approach to binary outcomes, was refined through backward stepwise feature elimination based on Akaike Information Criterion (AIC) values to ensure only the most relevant predictors remained. Elastic-net, a method combining lasso and ridge regression, was particularly useful for managing datasets with correlated features, as it both selects important variables and balances their influence. Random forest, an ensemble technique, creates a robust prediction model by merging results from multiple decision trees, thereby reducing overfitting and enhancing accuracy. Neural networks, composed of interconnected input, hidden, and output layers, were optimized to identify nonlinear relationships and model complex patterns within the data. SVMs, with their ability to identify an optimal boundary between two classes, utilized a radial basis function kernel to handle nonlinearity effectively. Finally, MARS (Multivariate Adaptive Regression Splines) generated segmented, localized models that adapted to the data's unique structure, simplifying the overall model while retaining accuracy through a pruning process that minimizes irrelevant features. Each of these strategies was fine-tuned using cross-validation to improve prediction robustness and relevance. An example of a recent paper that uses AI for this purpose is the study by [PERSON] et al. [152] in 2020. They utilized ensemble ML to interrogate the entire DVH to evaluate the relationships between dose-volume parameters and patient-reported health-related QoL changes. They applied ensemble learning methods, which combine multiple weak models, to improve prediction accuracy and robustness. They tested two main types: random forest, which builds multiple decision trees on random subsets, and boosting, which incrementally corrects errors. For boosting, we used Gradient Boosting Decision Trees (GBDT) and Adaptive Boosting, with GBDT building each tree to minimize prior errors. The ROC curve is widely recognized as a reliable tool for assessing binary classifiers' performance. However, caution is needed when applying it to imbalanced datasets. Alternatives like concentrated ROC (CROC), Cost Curves (CCs), and Precision-Recall plots (PRCs) have been proposed, although they are less common. Many studies still rely on ROC for performance evaluation, even with imbalanced data. [PERSON] et al. [153] propose that PRC plots, unlike ROC, visually highlight classifier sensitivity to imbalance and offer a straightforward, accurate interpretation of practical performance. Their findings strongly suggest that PRC plots are the most informative tool for visual performance analysis.