## 3 Results
### Diabetes Prediction
Income, HeartDiseaseOrAttack, DiffWalk, HighAlcoholConsumption, CholCheck: These features have low, but non-zero importance values, which means the model found them to have some predictive value, but they contribute far less to the prediction than the top factors. In clinical application, a plot like this helps practitioners and researchers concentrate their efforts on the most-telling risk factors when developing preventative measures and  
Figure 3: Feature importance plot for diabetes prediction. Figure 2: ROC curves for a Light Gradient Boosting Machine classifier used for diabetes prediction (diagonal line in the graph indicates that lines above it are significant or noteworthy). management strategies for diabetes. For example, interventions may focus on improving general health, since this is the most-influential feature; despite its vagueness, healthcare providers might emphasize comprehensive health improvement strategies that encompass diet, exercise, mental health, and chronic disease management. Managing high blood pressure: as the second-most-important factor, effective hypertension management could be a key focus for reducing diabetes risk. This could involve medication, dietary changes, and lifestyle modifications. Addressing obesity: given the BMI's role as a moderate predictor, weight management programs could be an important component of diabetes prevention efforts. Monitoring and support for aging populations: as age is a predictor, targeted screening and intervention programs could be developed for older individuals. Controlling cholesterol levels: since high cholesterol is a moderate predictor, cholesterol management through diet, exercise, and possibly medication could also be a priority. While the features with lower importance values contribute less to the model, this does not mean they should be ignored in clinical practice. Income, for instance, can influence health outcomes through complex social determinants of health. Heart disease, physical difficulty in walking, alcohol consumption, and cholesterol checkups are all parts of a patient's comprehensive health profile and, while less predictive in this model, still play roles in overall health and diabetes risk. Figure 4 shows the learning curves for the Light Gradient Boosting Machine classifier used to predict the diabetes. It shows the testing score starting above the cross-validation score and, then, declining to approach the cross-validation score as the number of training instances increases. This pattern suggests that, as the model is exposed to more varied data during training, it begins to generalize better. The initial high testing score might be a result of the model being too tailored to a small dataset and not capturing the true underlying patterns. When both scores converge, this indicates that adding more data is helping the model to stabilize. The testing score decreases to meet the cross-validation score, and this convergence signifies that the model is now generalizing well and its performance on the unseen test data is becoming consistent with the validation data used during training. A model that generalizes well is crucial in clinical settings because it indicates that the model can perform reliably when diagnosing new patients not seen during the training phase. The learning curve can also suggest whether more data could help to improve the model or whether the model has plateaued and additional data might not increase performance. Since the curve have not plateaued in this case, this indicates that the model has not reached its peak performance and additional training with more data would help to improve it. The validation curves in Figure 5 are a graphical representation showing the model performance (score) on both the training and validation datasets as a function of'max_depth' for the Light Gradient Boosting Machine classifier. The following in the validation curves for max_depth in a Light Gradient Boosting Machine classifier used to predict diabetes can be observed.