## 4 Discussion
In contrast, the SMOTEENN learning curves demonstrate healthiertraits, particularly at a sampling strategy of 200 instances, where a small gap between training and validation performance is evident, and the validation score shows a steady increase. This indicates a more balanced learning curve and improved generalization capabilities. However, the validation score worsens at higher sampling strategies, suggesting an optimal point for the number of instances beyond which the model's performance may degrade. The differences in values between SMOTE and SMOTEENN across Tables 1-5 suggest that SMOTEENN is a more effective technique for handling class imbalance in the context of the manuscript's study on fall risk assessment for bedridden patients. The higher accuracy and lower MSE values achieved by SMOTEENN indicate that it can lead to more accurate and reliable predictive models for fall risk assessment compared to using SMOTE alone. This improvement in predictive performance is crucial for developing effective real-time monitoring systems that can reduce the need for constant patient supervision. The slightly lower R\({}^{2}\) values for SMOTEENN on the training set and the higher R\({}^{2}\) values on the test set suggest that SMOTEENN may help the models generalize better when using unseen data. This is important for ensuring that the fall risk assessment models can perform well on new, real-world patient data. The consistent superior performance of SMOTEENN across different sample sizes (from 100 to 300) demonstrates its robustness and effectiveness in handling class imbalance, regardless of the number of instances per class. This is particularly valuable in the context of this particular dataset, as it suggests that SMOTEENN can be applied successfully to datasets with varying levels of class imbalance. By mitigating overfitting, SMOTEENN can contribute to the development of more reliable and generalizable fall risk assessment models. The performance of SMOTEENN at a sampling strategy of 300 instances is lower compared to the optimal point at 200 instances. This finding underscores the importance of identifying the right balance in data augmentation when using SMOTEENN. This study demonstrates that more instances do not always lead to better performance, and there is an optimal point that needs to be carefully determined. The slight decrease in validation score at higher sampling strategies, such as 300 instances, suggests that the model may be overfitting to the training data, leading to reduced generalization capabilities. This highlights the need for practitioners to thoroughly evaluate the impact of different sampling strategies on model performance when applying SMOTEENN to their specific datasets. The decreasing standard deviation for both methods as the number of instances increases suggests improved stability and generalization abilities with larger sample sizes. The lower standard deviation values for SMOTEENN, particularly at the 200-instance level, indicate that this method may provide more consistent and reliable performance across different cross-validation folds. The superior performance of SMOTEENN in terms of mean accuracy and stability suggests that this method could contribute to the development of more accurate and reliable real-time monitoring systems for fall risk assessment in bedridden patients. Despite its effectiveness in balancing skewed datasets, SMOTEENN has several limitations that could pose challenges for certain machine learning tasks. For one, this technique relies heavily on a proper parameter configuration, such as the number of nearest neighbors used both in the synthetic oversampling step (from SMOTE) and in the data-clearing step (from ENN). Incorrect parameter choices can either fail to eliminate noisy or borderline samples or, conversely, remove too many informative data points, thus impairing model accuracy. Moreover, the reliance on nearest-neighbor searches increases computational costs, particularly for large-scale or high-dimensional datasets.