###### Abstract  
The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library--a Python-based machine learning toolkit--to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics. PyCaret; machine learning; stroke diagnosis; diagnostic accuracy; automated machine learning; health informatics +
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can damage blood vessels and nerves that control the heart and blood vessels, leading to heart disease over time [9; 10; 11]. High blood glucose levels in diabetes can cause damage to blood vessels, which can result in serious heart complications [7; 8]. It is important for individuals with diabetes to manage their condition effectively to reduce the risk of developing heart disease or stroke. Managing diabetes through proper glucose control, regular exercise, a healthy diet, and other lifestyle modifications can help lower the chances of developing CVD [7; 8]. The management of diabetes mellitus involves a multidisciplinary approach in which both lifestyle and pharmacologic interventions play a role. The goals of management are to achieve normoglycemia and minimize cardiovascular and other long-term complications of the disease [12]. Studies following patients with intensive glycemic control found decreased incidences of major cardiovascular events in such patients [13]. In addition to pharmacologic management, patient education about the disease and long-term complications has been shown in studies to improve glycemic control in patients [14; 15; 16]. The role of behavioral modification on the effects of glycemic control has extensively been studied. Interventions such as fasting, low-calorie diets, exercise, and behavioral psychological therapy have all shown benefits in lowering blood glucose levels in studies assessing their effects in patients with diabetes [17; 18; 19; 20; 21]. One high-priority healthcare quality measure is hospital readmission rates. With many long-term complications, the burden of diabetes amongst hospitalized patients continues to grow. Studies have correlated higher rates of readmission for hospitalized patients with diabetes [22]. Adult patients with diabetes mellitus represent a sizable portion of 30-day unplanned hospital readmissions [23].