###### Abstract
Methods that can be implemented to reduce readmission rates include post-discharge coordination of care and support for patients with diabetes, regular follow-ups with providers, specialty-specific care, and patient education. Assessing readmission rates for patients with diabetes could benefit from additional studies studying the effects of such interventions. Machine learning has been explored as a tool to predict the incidence and risk factors of diabetes and other cardiovascular diseases [24]. Previous studies utilizing logistical regression and classification trees have predicted that fasting blood glucose, BMI, and age are the main predictors of developing diabetes and observed population health risk assessment for diabetes onset [25; 26; 27]. It has also been used to predict associated complications [28; 29]. However, there remains a gap in the literature when it comes to analyzing outcomes and readmission with the use of machine and deep learning. The present study conducted a comparative analysis of machine learning techniques to predict diabetes outcomes and readmission rates. The study used a dataset from the Kaggle repository [30]. The models were deployed in PyCaret 3.0.3 to predict diabetes and readmission rates. PyCaret has been previously utilized to predict the presence and severity of diabetes [31], but this approach can be applied to study associated outcomes. The study aimed to study machine learning methodologies while investigating their strengths and weaknesses pertaining to diabetes prediction and analysis of readmission rates. By leveraging large datasets and advanced algorithms, machine learning models can analyze complex patterns and risk factors to accurately diagnose diabetes. This can lead to more-timely interventions and improved patient outcomes. However, further research is needed to fully establish machine learning as a cornerstone of modern healthcare diagnostics for diabetes. Nevertheless, this study introduces an analytical framework that enhances the interpretability and clinical applicability of predictive modeling for diabetes and its readmission risk, using advanced machine learning techniques to not only analyze, but also translate the public Kaggle dataset's insights into actionable healthcare strategies, a critical advancement beyond the mere performance evaluation of algorithms. The study uncovers several insights into algorithmic predictions of diabetes readmission by exploring the intersection of machine learning and health informatics. Ultimately, this study highlights the need for additional research and development to fully integrate machine learning into modern diabetes care, enabling timely interventions and improved overall health outcomes. The structure of the paper is laid out as follows: Section 2, \"Materials and Methods\", details the data acquisition process from the public Kaggle dataset and the methodologies we applied in developing our analytical framework. Section 3, \"Results\", unpacks the performance metrics of our predictive models by delineating the rates of accurate classifications and misclassifications, as well as discussing the implications these have for clinical practice and patient outcomes. It also explores the model's capacity for generalization and its robustness, ensuring a comprehensive understanding of its practical utility. In Section 4, \"Discussion\", we contextualize our findings within the broader landscape of healthcare analytics and their implications for diabetes management.