## 2 Materials and Methods
### PyCaret Setup  
During the setup of PyCaret in this study, we initialized the environment with a consistent random state to maintain reproducibility. We then proceeded with the data preparation, detailing the handling of missing values and the encoding of categorical variables, ensuring clarity on the strategies employed. Feature scaling methods were applied to normalize numerical features, essential for certain algorithms. Outliers were managed through PyCaret's built-in capabilities or through additional steps, as necessary. In selecting and training the models from PyCaret's extensive library, we clarified our criteria, including any baseline models used for comparison. For hyperparameter tuning, we specified the search algorithm utilized and the hyperparameters' range. Model evaluation and validation were detailed by describing the use of performance metrics and validation techniques such as cross-validation. Finally, the criteria for the final model selection were carefully outlined, balancing performance metrics with considerations of complexity and interpretability, to ensure a comprehensive and transparent portrayal of our research methodology. The PyCaret setup for both the DSD and DRD followed the same process and included similar transformation steps. To begin, both datasets were converted to comma-separated values (CSV) format, with each file containing a target variable. In the case of the DSD, the target variable represented the presence or absence of diabetes. For the DRD, the target variable had three options: no readmission, readmission within 30 days, and readmission after 30 days. Next, the datasets were split into training and testing categories. The DSD contained a total of 70,692 individual patient records. This dataset was divided into a training set consisting of 49,484 patient records and a testing set consisting of 21,208 patient records. Similarly, the DRD included a total of 101,766 patient records. This dataset was split into a testing set of 71,236 patient records and a training set of 30,530 patient records. For the preprocessing step applied to both the DSD and DRD, a simple imputer was used to handle missing values in the dataset. Beyond handling missing values with a simple imputer, we also employed scaling techniques to normalize the range of continuous input variables, which is crucial for models that are sensitive to the varying scales of features. Additionally, categorical variables were encoded into numerical values to ensure compatibility with our machine learning algorithms, as they require numeric input. Outliers were treated with careful consideration: depending on their nature and the potential impact on our model's ability to learn from the data, certain outliers were either adjusted or removed to prevent them from skewing the results. Collectively, these preprocessing steps were applied to each feature to optimize the performance of our predictive models and ensure that any conclusions drawn from our analysis were based on data that were as accurate and representative as possible. Additionally, in this predictive modeling, preprocessing steps with the specific requirements and assumptions of the algorithms used were aligned. Missing values were handled with imputation techniques since most algorithms necessitate complete datasets to avoid biases and errors during training. Categorical variables were encoded to numerical values to ensure compatibility across all models, with the encoding method chosen based on the