## 3 Results
### Diabetes Readmission Prediction
It is important for clinicians and healthcare administrators to consider these values when deciding how to incorporate the model into practice. When using this model, it may need to be combined with other clinical assessments and risk stratification tools to make more-informed decisions about patient care following discharge. The feature importance plot for diabetes readmission prediction is shown in Figure 8. The plot shows that AvgGlucoseLevel holds the greatest importance. This implies that the average blood glucose level of a patient is the strongest predictor for whether a patient with diabetes might be readmitted to the hospital. It is clinically meaningful as glucose levels need to be managed effectively in diabetics to prevent complications. BMI has the second-highest importance value. This reflects that a patient's body weight relative to his/her height is a significant factor in readmission risk, likely due to obesity being a risk factor for diabetes complications. Lower values are attributed to features like WorkType, EverMarried, Hypertension, SmokingStatus, HeartDisease, and ResidenceType. Although these have an association with the risk of readmission, their influence is stronger than factors like sex and age, but less than AvgGlucoseLevel and BMI. These could represent lifestyle and social determinants of health that impact patient outcomes to a lesser degree. Finally, sex and age are given zero values, suggesting that, in this particular model, they do not contribute to predicting readmission for patients with diabetes. This does not mean these factors are not important in other contexts, but in this specific model's configuration, their impact is non-existent or negligible. This hierarchy in feature importance can help healthcare providers focus their interventions and follow-up strategies on what matters most for preventing readmissions in diabetic patients. They may focus more on controlling blood glucose levels and managing weight, while still being aware of other factors that could influence a patient's risk of returning to the hospital. In Figure 9, the following characteristics in the learning curves for a Light Gradient Boosting Machine classifier used for diabetes readmission prediction can be observed. Since the training curve is constant at 1, it suggests that the LightGBM model is perfectly fitting the training data throughout the training process, from the smallest to the largest number of training instances. A constant score of 1 indicates that the model has 100% accuracy on the training dataset, which often points to overfitting. The increasing of the cross-validation score curve signifies that, as more data are provided for training, the model's performance on unseen data (the validation set) is improving. Initially, when the number of training instances is small, the model may not have learned the underlying patterns adequately, which is reflected in a lower cross-validation score. As training progresses and the model is trained on more data, it begins to generalize better, capturing the essential patterns to make more-accurate predictions on unseen data. The cross-validation score approaching 1 towards the end of the training process means that the model's predictive performance on unseen data is nearly perfect. This can be an indicator that the model has learned the  
Figure 8: Feature importance plot for diabetes readmission prediction. underlying trends in the data quite well and is able to generalize this learning to new data effectively. In the clinical context, learning curves with such characteristics have both positive and negative implications. A perfect training score is often a red flag for overfitting. In a clinical setting, an overfitted model may perform exceptionally well on historical data, but fail to predict accurately on future patients or those from different demographic groups. Therefore, the clinical reliability of such a model might be questionable.