## 5 Conclusions  
Provided below is a concise summary of the study's principal findings and viewpoints, structured as bullet points. These encapsulate the study's contributions to the use of machine learning in healthcare, highlighting the capability to improve diabetes management and identifying paths for subsequent research:  
* Diabetes mellitus is a metabolic condition that leads to high blood glucose levels, increasing the risk of cardiovascular disease and requiring a multidisciplinary management approach.
* The study explores the use of machine learning, specifically the PyCaret library, in predicting and managing diseases, with a focus on diabetes mellitus and hospital readmission rates.
* Machine learning has been used to predict the incidence and risk factors of diabetes and other cardiovascular diseases, using a dataset from the Kaggle repository.
* The Light Gradient Boosting Machine classifier was identified as a prominent algorithm due to its high performance metrics, particularly in predicting late readmissions.
* However, the model was less reliable in classifying patients who are not likely to be readmitted or those who might be readmitted within 30 days.
* There is a need for further research and validation across diverse clinical settings to fully incorporate machine learning into modern diabetes care.
* The study highlights the importance of balancing different performance metrics and considering the specific clinical requirements and costs associated with different types of diagnostic errors.
* The study affirms the value of machine learning models in addressing complex clinical questions and improving the accuracy and timeliness of diagnoses.
* The findings ought to be contextualized within the broader landscape of healthcare analytics and their implications for diabetes management.
* Future research should focus on external validation and explore the utility of these models across various patient demographics and healthcare infrastructures.  
**Author Contributions:** Conceptualization, R.J., F.S., A.T. and M.T.; methodology, R.J., F.S., A.T. and M.T.; software, R.J., F.S., A.T. and M.T.; validation, R.J., F.S., A.T. and M.T.; formal analysis, R.J., F.S., A.T. and M.T.; investigation, R.J., F.S., A.T. and M.T.; resources, R.J., F.S., A.T. and M.T.; data curation, R.J., F.S., A.T. and M.T.; writing--original draft preparation, R.J., F.S., A.T. and M.T.; writing--review and editing, R.J., F.S., A.T. and M.T.; visualization, R.J., F.S., A.T. and M.T.; supervision, M.T.; project administration, M.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets analyzed in this study, as well as the software used for their analysis, are available online. The corresponding references to these datasets and software are included in the article. Any additional information or data not provided in the article can be obtained upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CVD & cardiovascular disease \\ BMI & body mass index \\ CAD & coronary artery disease \\ DSD & Diabetes Surveillance Dataset \\ BRFSS & Behavioral Risk Factor Surveillance System \\ DRD & diabetes readmission dataset \\ AUC & area under the curve \\ MCC & Matthews correlation coefficient \\ TT & training time \\ ROC & receiver operating characteristic \\ \end{tabular}