## 3 Results
### Diabetes Prediction
This is important for avoiding unnecessary treatments or further medical investigation for those who are healthy. The classifier incorrectly predicted 21% of the non-diabetic patients as diabetic. Clinically, this could lead to unnecessary stress for the patients, added healthcare costs, and potentially unnecessary treatment, which can have side effects. The classifier failed to identify 28% of the actual diabetic patients, indicating that these individuals were incorrectly classified as non-diabetic. This is particularly concerning in a clinical environment as it means that nearly a third of diabetic patients might not receive the necessary medical attention promptly. This could result in the progression of the disease and associated complications since early intervention and management of diabetes are crucial for optimal health outcomes. In Figure 2, the ROC curves for a Light Gradient Boosting Machine classifier used for diabetes prediction are shown, i.e., curves for each class and additional curves for the macro-average and micro-average AUC. In the context of diabetes prediction, since there are only two classes (diabetic and non-diabetic), Classes 0 and 1 indicate 'non-diabetic' and 'diabetic' patients, respectively. In this case, since both Class 0 and Class 1 have AUCs of 0.83, the model is equally good at identifying diabetic, as well as non-diabetic patients. The macro-average and micro-average ROC values having AUCs of 0.83 suggest that, overall, the model has balanced performance across both classes without any bias towards one class due to imbalance in the dataset. In clinical settings, such a model indicates a reliable level of discrimination for diagnosing diabetes and could, thus, be used to support clinical decision-making. The feature importance plot, shown in Figure 3, visualizes the significance of different features used by the model to predict diabetes. A feature importance plot for diabetes prediction visually represents the relative significance of various predictors in a machine learning model's decision-making process. In this plot, GenHlth holds the highest importance in the model, suggesting that overall self-reported health is the strongest predictor of  
Figure 1: Light Gradient Boosting Machine classifier confusion matrix for diabetes prediction. diabetes among the features evaluated. Clinically, this could signify that how individuals perceive their health is a good indicator of underlying conditions that may include or lead to diabetes. However, general health as a feature is quite vague because it encapsulates various aspects of an individual's overall well-being. This may include factors like diet quality, physical activity levels, mental health status, and the proper management of any existing chronic conditions. The vagueness arises from the complexity and breadth of what constitutes \"general health.\" Each component (diet, exercise, etc.) alone can have a multitude of dimensions and can be affected by subjective self-reporting and measurement inconsistencies. Because general health is a broad concept, when it is used as a significant feature in predictive modeling, it may provide a strong signal due to its comprehensive nature. With about half the importance value of GenHlth, high blood pressure is also a significant predictor, but less so than general health status. This reflects the known association between hypertension and the risk of developing diabetes [38]. BMI, age, and HighChol: each of these features has an importance value around one quarter that of GenHlth, indicating moderate predictive power. These factors are well-established risk factors for diabetes: a higher BMI can indicate obesity [39]; age is a known risk factor as risk increases with age [40]; high cholesterol levels are associated with increased cardiovascular risks and can be indicative of metabolic issues related to diabetes [41].