## 1 Introduction
However, these conclusions are mainly based on the assumption that the risk of diabetes in women is the same as in men [14]. The sex-based difference in the risk of diabetes may not only be due to patient management and treatment but also to diverse biological factors [15]. Consequently, this domain has garnered significant scholarly attention in recent years [5; 11]. The exponential growth of healthcare data is significantly accelerating the transition towards precision and personalized medicine. Artificial intelligence (AI) and algorithmic methodologies are becoming crucial to enhancing clinical decision-making, healthcare delivery, and the provision of health services. AI, employing advanced computer science techniques and massive datasets, is increasingly vital in medical research, as evidenced by its applications across a spectrum of studies in biology and medicine. Particularly in the realm of type 2 diabetes, AI tools, such as machine learning and neural networks, not only facilitate therapeutic monitoring but are also instrumental in predicting the onset and future complications of type 2 diabetes [16; 17; 18]. These technological advancements streamline routine operations, thus empowering healthcare providers to focus on more critical issues and improve the efficiency of medical care [19]. For instance, these tools enable patients to perform preliminary self-assessments of their type 2 diabetes status using personal physiological data, which aids physicians in making faster and more accurate diagnoses. Moreover, the implementation of AI in predicting disease patterns is anticipated to contribute to a reduction in the global prevalence of type 2 diabetes, which currently stands at 8.8% [20]. This demonstrates the profound impact AI is poised to have on global health outcomes and the management of chronic diseases such as type 2 diabetes. The management of type 2 diabetes is notably complex, affected by a variety of sociodemographic, medical, and process-related factors, which cumulatively contribute to the challenge of mitigating readmission risks for patients with this condition. Beyond the systemic issues within healthcare settings, patient-specific factors play a crucial role in influencing readmission rates [21]. Notably, poor health literacy emerges as a significant barrier, as effective type 2 diabetes management necessitates a higher level of patient involvement than many other chronic illnesses. Patients with limited health literacy are often unable to recall or fully understand their discharge instructions, which significantly heightens their risk of readmission. Although numerous clinical trials have been conducted with the aim of reducing readmission rates, the focus has predominantly been on broader hospital populations, including unselected hospitalized patients, medical service users, the elderly, or those admitted for conditions like heart failure. These studies have reported reductions in 30-day readmission risks ranging from 30% to 75%. However, the specific challenges associated with type 2 diabetes have received less attention, underscoring the need for targeted research [5; 22]. To demonstrate the effectiveness of our approach, this study utilized comprehensive clinical data from the MIMIC-III repository, focusing on patients admitted to the ICU at Beth Israel Deaconess Medical Center in Boston, MA. The dataset provides detailed patient information, including laboratory test results, demographic characteristics, microbiology findings, treatment courses, and fluid volumes in and out. The MIMIC-III dataset is extensively used in a range of research areas, including clinical prediction, disease diagnosis, and treatment strategies, and has been featured in numerous studies and publications across various domains. By leveraging machine learning to identify patterns in readmissions among type 2 diabetes patients, our study aims to develop customized interventions to effectively reduce these rates.