## 4 Discussion
To reduce false positives in the future, efforts should focus on ensuring high-quality data and possibly reevaluating the features used for prediction. Consideration might also be given to employing class balance techniques, adjusting the decision-making threshold, and applying regularization techniques to improve the model's generalizability. These steps are crucial for making the model more reliable and, thus, more useful in clinical applications where accurate predictions can significantly impact patient outcomes. The models discussed in the study can uncover the influence of feature interrelations that might not be evident when considering individual feature's impacts in isolation. Machine learning techniques, especially those encompassing complex models like ensemble methods or deep learning, are capable of capturing nonlinear interactions and dependencies between features. For instance, a single variable might have minimal predictive power on its own, but when combined with others, it can contribute significantly to the model's accuracy. By examining feature importance plots and analyzing the model's decision-making process, insights into complex interactions can be deduced. Understanding these interrelations is critical because it can lead to more-accurate predictions and can inform clinicians about synergistic or antagonistic effects between variables. This might lead to the discovery of previously unknown patterns in the data, influencing decisions regarding treatment plans or follow-up strategies. It highlights the need for careful consideration of the model's interpretability and the use of appropriate tools to shed light on how different features influence model predictions. It is essential to focus on the potential impact of these models on patient outcomes and the healthcare system in general. These models must deliver actionable insights that improve the accuracy and timeliness of diagnoses, guiding interventions that mitigate the risk of complications and readmissions. Clinicians rely on the outputs of these models to make decisions, so the models must be trustworthy and their predictions should align with real-world clinical scenarios. The effectiveness of a predictive model is not solely determined by its statistical accuracy; it also depends on its ability to support healthcare providers in making decisions that lead to better care and resource utilization. A model that accurately identifies patients at high risk for diabetes complications or readmission can help in allocating medical resources more effectively and possibly preventing worse health outcomes. Conversely, a model must also avoid overburdening the healthcare system with false alerts that could lead to unnecessary tests or treatments. Ultimately, incorporating predictive models into clinical practice involves a careful consideration of these trade-offs to ensure that the models serve their intended purpose--to support clinicians in delivering optimal care to diabetic patients and efficiently manage hospital resources. For predicting diabetes readmission, the metrics achieved by the Light Gradient Boosting Machine classifier indicate its utility in prognostic settings. The ability to accurately predict readmissions can profoundly affect patient management, potentially preventing the adverse outcomes associated with early rehospitalization. This outcome is particularly vital as it enables healthcare providers to identify individuals most at risk and deploy interventions preemptively, thus enhancing patient care and optimizing resource utilization. The dynamic nature of diabetes progression and readmission rates demands that predictive models are not only accurate, but also adaptable. Our learning curves for the Light Gradient Boosting Machine classifier point towards a model that benefits from more data. Similarly, the validation curves suggest that models need to be finely tuned to generalize well to unseen patient data undefined.