## 5 Conclusions  
Predicting mortality and readmission rates for patients in the ICU is crucial for optimizing healthcare expenditures and improving patient outcomes. Identifying high-risk individuals enables healthcare providers to implement customized treatment strategies and proactive interventions such as personalized care plans and rigorous health monitoring. This approach alleviates pressure on healthcare infrastructure while ensuring timely and appropriate patient care. Utilizing predictive models for high-risk groups, such as the elderly, immunocompromised individuals, and patients with chronic conditions, enables clinicians to effectively reduce mortality and readmission risks. Consequently, this enhances the efficiency and efficacy of healthcare services, contributing to overall improvements in healthcare delivery. Given the ongoing global rise in the number of patients with type 2 diabetes and their significantly increased risk of complications, precise clinical predictions for this population have become increasingly important. Such predictions can significantly improve treatment outcomes for type 2 diabetes patients while also alleviating the burden on healthcare systems.  
In this study, we utilized the MIMIC-III database to integrate quantitative data collected during patients' ICU stays. Our objective was to employ machine learning to predict mortality and readmissions among ICU patients. Advanced machine learning techniques, such as bagging and Adaboost, have demonstrated significant potential in accurately predicting mortality and readmission rates in patients with type 2 diabetes. Our methodology accomplishes several key objectives and provides substantial benefits for healthcare practitioners and patients alike. This approach enhances the standard of care by enabling early interventions that effectively reduce both mortality and readmission rates. It also addresses the financial burden associated with these issues through proactive patient management and timely intervention. Utilizing predictive analytics, hospitals can create customized discharge plans tailored to patients' risk levels, incorporating more intensive follow-up and personalized rehabilitation to further decrease readmission rates. Machine learning models provide valuable decision-support tools, allowing clinicians to make more accurate assessments and develop more effective treatment strategies. By implementing real-time analysis, hospitals can establish early warning systems to identify high-risk patients promptly. This capability facilitates personalized medical interventions, optimizes resource allocation, and improves bed utilization while minimizing unnecessary readmissions. Overall, this approach not only enhances patient care but also boosts the efficiency of healthcare resource management. Moving forward, it is imperative to further refine and validate our predictive model in real-world clinical settings. Continued research efforts are essential to explore the broader implications of our findings on the healthcare system and patient outcomes. By advancing the application of machine learning tools in healthcare, we can ultimately drive improvements in patient care delivery and contribute to the advancement of precision medicine.  
Conceptualization, T.-L.H., C.-M.C. and C.-C.W.; Data curation, C.-C.W., C.L. and T.-N.C.; Formal analysis, C.-C.W., C.L. and T.-N.C.; Methodology, T.-L.H., C.-M.C., C.-C.W., C.L. and T.-N.C.; Supervision, T.-L.H., C.-M.C., C.-C.W. and T.-N.C.; Writing--original draft, C.-C.W. and T.-N.C.; Writing--review and editing, C.-C.W., C.L. and T.-N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors would like to sincerely thank the editor and reviewers for their kind comments.  
The authors declare no conflicts of interest.