## 4 Discussion
### Readmission Principal Findings  
The aggregation of advanced machine learning techniques and EHR data offers a promising avenue for enhancing healthcare delivery, particularly in critical care settings such as ICUs. Our study highlights the importance of leveraging artificial intelligence and machine learning methods to develop predictive models with which to assess post-discharge readmission risk, particularly for patients with type 2 diabetes. Despite the increasing importance of avoidable readmissions as a key metric for assessing the quality of hospital care, existing machine learning-based models have shown only limited performance, typically producing AUC scores of 0.619-0.686. [PERSON] et al. applied three time series machine learning models, namely, random forest, naive Bayes, and tree ensemble, to predict AUROC for 30-day readmission rates. The readmission prediction AUC values for random forest, naive Bayes, and tree ensemble were reported as 0.640, 0.619, and 0.634, respectively [40]. In another study, [PERSON] and [PERSON] conducted a comparative analysis of various machine learning algorithms to evaluate their performance in classification tasks. In their study, they employed the following five different algorithms: logistic regression, decision tree classifier, random forest, AdaBoost, and gradient boosting. The results indicated that the accuracy of these models ranged from 0.579 to 0.665, while the F1 scores were observed to be between 0.673 and 0.783. These findings were published in their research paper, which contributes to the ongoing discourse on the efficacy of machine learning models in predictive analytics [41]. In comparison, our proposed method achieves an AUC score of 0.84, which is better than previous studies. These findings highlight the effectiveness of combining natural language processing techniques with quantitative data in accurately predicting discharge readmission rates. Our findings demonstrate that machine  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1 Score** & **Accuracy** \\ \hline Adaboost & 0.5306 \(\pm\) 0.0162 & 0.8298 \(\pm\) 0.0213 & 0.6473 \(\pm\) 0.0087 & 0.9249 \(\pm\) 0.0075 \\ Bagging & 0.5175 \(\pm\) 0.0256 & 0.8404 \(\pm\) 0.0348 & 0.6405 \(\pm\) 0.0103 & 0.9216 \(\pm\) 0.0088 \\ GaussianNB & 0.4660 \(\pm\) 0.0218 & 0.7868 \(\pm\) 0.0181 & 0.5848 \(\pm\) 0.0159 & 0.9087 \(\pm\) 0.0065 \\ Logistic Regression & 0.4514 \(\pm\) 0.0096 & 0.7986 \(\pm\) 0.0337 & 0.5763 \(\pm\) 0.0049 & 0.9042 \(\pm\) 0.0048 \\ MLP Classifier & 0.4552 \(\pm\) 0.0385 & 0.8541 \(\pm\) 0.0268 & 0.5921 \(\pm\) 0.0260 & 0.9031 \(\pm\) 0.0141 \\ SVC & 0.4801 \(\pm\) 0.0124 & 0.7698 \(\pm\) 0.0273 & 0.5909 \(\pm\) 0.0091 & 0.9130 \(\pm\) 0.0045 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The readmission classification model performance metrics.  
learning models, including AdaBoost, bagging, GaussianNB, logistic regression, MLP, and SVC, have significant success in predicting the odds of readmission in patients with type 2 diabetes. Notably, the MLP and Adaboost models showed excellent performance with an AUC score of 0.8487 and an accuracy score of 0.9249. These results highlight the potential of machine learning algorithms to identify high-risk patients who may benefit from additional monitoring or interventions to prevent readmissions.