## 3 Results
### Diabetes Prediction  
In Table 2, LightGBM performs best on most metrics, but has a slightly lower AUC and precision compared to the Gradient Boosting Classifier and ADA/LR, respectively. The AUC measures the model's ability to differentiate between classes (e.g., diabetic and non-diabetic patients). An AUC of 0.8302 for LightGBM and 0.8309 for the GBC suggests that both models have a strong ability to discriminate between positive and negative cases, with the GBC being marginally better. In practice, this difference might not be clinically significant, but it indicates that the GBC could potentially provide slightly more-accurate risk stratification. Precision assesses how many of the patients predicted to have the condition (positive cases) actually do. LightGBM's precision score of 0.7320 compared to the ADA and LR's precision score of 0.7392 suggests that, while both models are quite good at ensuring that positive predictions are likely true, the ADA and LR are slightly better. This means using the ADA or LR might result in fewer false positives--patients incorrectly identified as having the condition. While LightGBM's lower scores in the AUC (0.8302) and precision (0.7320) compared to the GBC and ADA/LR, respectively, mean that there are some trade-offs to consider, it is critical to understand that machine learning predictions are not diagnostic tools themselves, but rather, risk stratification aids that would be used in conjunction with further diagnostic testing. Hence, LightGBM's high performance across most metrics indicates that it is a robust model for predicting diabetes. Clinically, the decision on which model to use could factor in the degree of variance in the metrics. If false positives are particularly undesirable (e.g., could lead to unnecessary anxiety, more-invasive tests, or treatments), a physician might lean towards the ADA and LR due to the higher precision. If the overall ability to distinguish between patients is paramount and the differences in the precision are acceptable, LightGBM could still be preferred given its general performance on most metrics. When these differences in scores are very narrow (as they appear to be between these models), it becomes important to consider other factors such as the models' interpretability, operational costs, and how well they integrate into clinical workflows. Clinicians might also evaluate the models based on specific patient cohorts or different clinical settings, given that the performance could vary across different scenarios. The confusion matrix for the Light Gradient Boosting Machine classifier in a diabetic prediction scenario is shown in Figure 1. The confusion matrix provides a snapshot of how well the Light Gradient Boosting Machine classifier is performing in accurately diagnosing diabetes as follows. The true positive rate (72%) reflects the model's ability to identify most of the individuals with diabetes. The true negative rate (79%) shows that the model is also quite capable of recognizing individuals who do not have the condition. The false positive rate (21%) points to the rate at which the model incorrectly flags healthy individuals as diabetic, which could lead to unnecessary worry and medical procedures for these patients. The false negative rate (28%) is a critical metric in clinical settings, as it represents the missed cases of diabetes that were not diagnosed by the model. In a clinical setting, interpreting this confusion matrix gives us insights into how well the Light Gradient Boosting Machine classifier performs in diagnosing/predicting diabetes. The model correctly predicted 72% of the diabetic patients. This implies that, out of all patients who were actually diabetic, the classifier could identify 72% of them. In a clinical setting, this is a favorable outcome since it indicates successful detection of the majority of individuals with diabetes. The model correctly predicted 79% of non-diabetic patients. This means 79% of the patients who did not have diabetes were correctly identified by the model.