## 3 Results  
In this section, a comprehensive evaluation of machine learning models developed using the PyCaret library to predict diabetes and its subsequent readmissions is presented. Our analysis comprises two distinct subsections: the first focuses on the deployment and performance of various algorithms in accurately predicting the incidence of diabetes mellitus, while the second delves into the predictive modeling of diabetes readmission rates. Each subsection systematically illustrates the effectiveness of the employed models, discussing metrics such as the accuracy, precision, recall, and area under the receiver operating characteristic curve. By dissecting the models' performances in these two pivotal areas of diabetes care, this section aims to shed light on the potential and limitations of automated machine learning in effectively supporting disease management and healthcare delivery. Tables 2 and 3 are structured comparisons that outline the performance of various machine learning classifiers on diabetes prediction and readmission prediction, respectively. Each classifier is evaluated according to several important metrics that gauge their performance as follows. Accuracy: This is the ratio of the number of correct predictions to the total number of input samples. It measures the overall correctness of the model. Higher accuracy means the model is more often correct across all predictions. Area under the curve (AUC): This metric is associated with the receiver operating characteristic curve and reflects a model's ability to distinguish between the classes (positive as diabetic and negative as non-diabetic). A higher AUC value indicates better discrimination capabilities. Recall (or sensitivity): Recall indicates how many of the actual positive cases (actual diabetics) the model correctly identified. It is crucial for conditions where missing a positive case can have serious consequences. Precision: This measures the fraction of correct positive predictions out of all positive predictions made. In the context of diabetes, it reflects how many of the patients the model labeled as diabetic were actually diabetic. F1-score: The F1-score is the harmonic mean of the precision and recall, providing a balance between the two. It is especially useful when the distribution of classes is not even, which is often the case in medical diagnoses. Cohen's Kappa: This measures the agreement of the predictive model with the actual data, correcting for chance agreement. It gives a more-robust idea of the model's performance, especially with imbalanced datasets. Matthews correlation coefficient (MCC): This is a measure of the quality of binary classifications. It takes into account true positives, true negatives, false positives, and false negatives. The MCC is  
considered a balanced measure, which can be used even if the classes are of very different sizes. Cells highlighted in yellow within Tables 2 and 3 emphasize standout performance metrics or notable findings in the machine learning model comparison. These highlights are used to draw the attention to results that significantly impact the study's conclusions, such as the highest accuracy rates, the best precision values, or any unexpected patterns in the data that may require further investigation or discussion. In clinical decision-making, the significance of evaluation metrics such as accuracy, precision, recall, and AUC extends beyond their numerical definitions to their impact on patient care, resource allocation, and overall health outcomes. Accuracy is the proportion of true results, both true positives and true negatives, in the total population. It is a straightforward measure and gives an overview of the model's effectiveness. However, in a clinical setting, high accuracy is not always indicative of practical utility, particularly in cases where disease prevalence is low. A model could inaccurately predict true disease cases, yet still have high accuracy due to a large number of true negatives--healthy patients correctly identified as not having the disease.