## 3 Results
### Diabetes Readmission Prediction
x32 = 15%: percentage of patients incorrectly predicted as'readmitted before 30 days' when the readmission actually occurred after more than 30 days. x13 = 59%: percentage of patients incorrectly predicted as'readmission after more than 30 days' with no actual readmission. x23 = 63%: percentage of patients incorrectly predicted as'readmission after more than 30 days' when they were actually readmitted before 30 days. x33 = 84%: percentage of patients correctly predicted as'readmission after  
that the model is particularly effective at identifying patients at risk of readmission more than 30 days after discharge, which could be valuable for long-term patient care planning and intervention. The relatively low true positive rates for conditions 0 and 1 (3% and 37%, respectively) indicate that the model is less reliable at classifying patients who are not likely to be readmitted, as well as those who might be readmitted within 30 days. This might lead to some degree of unnecessary resource allocation for individuals less at risk while potentially under-serving those at higher risk of short-term readmission. The incorrect predictions, such as patients without readmission being classified as readmitted before 30 days (38%) or after more than 30 days (59%), could result in unnecessary follow-up, adding to patient anxiety and burdening the healthcare system. Conversely, the model's incorrect predictions in missing true cases of readmission within 30 days (i.e., x21 and x23) represent a risk of not providing necessary care for those patients, which could lead to worse health outcomes. In practice, these results would necessitate a balance between managing the risks of unnecessary follow-ups (resulting from false positives) versus the risk of missing early interventions (due to false negatives). This balance is important for the quality of patient care, clinical workflow, resource allocation, and healthcare costs. Figure 7 illustrates the ROC curves for a Light Gradient Boosting Machine classifier that is used for predicting diabetes readmission. Each ROC curve corresponds to one of the readmission categories, and the area under each curve provides a measure of how well the model distinguishes between classes. The ROC of class 0, AUC = 0.67: This is the ROC curve for patients with no readmission. An AUC of 0.67 suggests that the model has a moderate ability to distinguish between patients who will not be readmitted and those who will. ROC of class 1, AUC = 0.66: This curve pertains to patients readmitted before 30 days. The AUC value indicates that the classifier's ability to differentiate between those readmitted within 30 days and all other outcomes is slightly less than moderate. ROC of class 2, AUC = 0.69: This relates to the patients readmitted more than 30 days after discharge. An AUC of 0.69 shows that the model is moderately good at differentiating between late readmissions and the other outcomes. Macro-average ROC, AUC = 0.78: The macro-average AUC is calculated by taking the average of the AUC values for each class. An AUC of 0.78 is relatively high, indicating good overall discriminatory ability across all classes without taking class imbalance into account. Micro-average ROC, AUC = 0.67: The micro-average AUC aggregates the contributions of all classes to compute the average AUC. A value of 0.67 suggests that the model has a moderate overall performance at distinguishing between classes when considering class imbalance. Clinically, the AUC values provide insight into the model's predictive power. An AUC closer to 1 indicates excellent model performance, while an AUC closer to 0.5 suggests that the model is no better than random chance. In this case, the LightGBM classifier shows moderate predictive performance for individual classes, and the macro-average indicates a  
Figure 7: ROC curves for a light gradient boosting machine classifier used for diabetes readmission prediction. better overall performance.