## 4 Discussion
Surprisingly, the study revealed disparities in the predictive models' ability to detect early versus late readmission. This highlights an opportunity for further investigation into model tuning to enhance early readmission predictions, which are crucial for immediate post-discharge care and planning. This study utilizes open-source datasets, specifically the DSD and the DRD from the UC Irvine Machine Learning Repository, due to their wide use and accessibility within the research community. It is important to note, however, that the reliance on these datasets introduces certain limitations regarding the quality and the representativeness of the data. Such datasets may not capture the full spectrum of the diabetic population, potentially impacting the generalizability of our model's findings. The representativeness of the patient data from these datasets, as compared to the broader diabetes population, should be critically considered when interpreting the results of this study. Further research using a diverse range of datasets, including those that more accurately reflect the current demographic makeup of the diabetic community, would be beneficial for confirming the validity and improving the applicability of our predictive modeling approach. This is a recognized challenge within the field and one that is important for the advancement of machine learning tools aimed at diagnosing and managing diseases like diabetes. Additionally, the discussion of overfitting reflects a recurrent challenge in machine learning. Our learning and validation curves indicated the necessity for optimal complexity in our models. The drive to improve sensitivity must be carefully weighed against the risk of generating models too finely tuned to training data that they fail to generalize to new patients. Therefore, continuous refinement of the balance between model complexity and generalizability is essential. Hence, while the application of the PyCaret library in predicting diabetes and readmission has shown promise, further exploration and validation are required. The inescapable trade-offs between different performance metrics point to the necessity for an interdisciplinary approach that combines machine learning expertise with clinical knowledge. The efficacious application of a Light Gradient Boosting Machine classifier for diabetes prediction and diabetes readmission prediction in our analysis underscores this synergy. These models have exhibited a balance between capturing complex patterns in the data and avoiding the pitfall of overfitting. Our findings suggest that the Light Gradient Boosting Machine classifier, with its capability to handle various types of features and data distributions, stands out for its diagnostic accuracy for diabetes. This has broad implications for the early identification of diabetic patients, which is crucial for the timely initiation of the treatment and prevention of acute complications. The model's performance was particularly strong in distinguishing between diabetic and non-diabetic cases, a result that may be attributed to its iterative approach, which focuses on correcting the misclassified observations in successive training rounds. However, machine learning models with a 20-30% false positive rates indicate that the models misclassified about one-quarter of the negative cases as positive, such as predicting that patients will be readmitted when they will not be. This could be due to a variety of factors, including imbalanced datasets, where one class predominates, leading to bias; inadequate or noisy features that fail to capture the essence of the predictive patterns; improperly calibrated decision thresholds that tip the balance towards positive predictions; overfitting, where the model fits the training data including their noise too closely and fails to generalize; or underfitting, where the model is too simplistic to capture the underlying structure of the data. It might also be attributed to data quality issues, such as inaccuracies in the data that the model was trained on.