# Prediction of Diabetes Complications Using Computational Intelligence Techniques
## 4 Results and Performance Evaluation
The outcome of whether the individual had diabetes (1 = yes, 0 = no) was also considered. Using the data collected, prediction models for the risk of developing diabetes were created to potentially prevent the disease in the future. The accuracy of these models is presented in Table 5, where it can be seen that the XGBoost classifier had the highest accuracy of 89%. This indicates that the XGBoost classifier can correctly predict diabetes risk 89% of the time when using the input data. The experimental results presented in Table 5 indicate little difference in performance between the single models used (LR, KNN, CART, RF, SVM, XGBoost, and LightGBM \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifiers** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline LR & 0.84 & 0.85 & 0.84 & 0.86 \\ KNN & 0.84 & 0.84 & 0.88 & 0.84 \\ CART & 0.85 & 0.85 & 0.87 & 0.85 \\ RF & 0.88 & 0.88 & 0.88 & 0.88 \\ SVM & 0.85 & 0.86 & 0.86 & 0.85 \\ XGB & 0.89 & 0.89 & 0.90 & 0.89 \\ LightGBM & 0.88 & 0.88 & 0.87 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the generated prediction models. Figure 7: Confusion matrix. methods). On the test dataset, the XGBoost model achieved 89% accuracy in predicting the occurrence of diabetes. In comparison, the LightGBM model achieved 88% accuracy, considered a standard statistical analysis method at the time. This study used a large dataset and ensemble machine learning approaches to create the prediction models, which is different from previous works. A data-driven feature selection method was used to develop predictors that effectively identified the different classes in the dataset. The statistics for the different classifiers for the accuracy of the proposed system showed the highest values for the dataset, as shown in Figure 8. Furthermore, by adjusting the number of iterations used to train the models, the study also demonstrated the effect of accumulated medical data on prediction accuracy. This approach aims to improve the accuracy of diabetes prediction and early identification. The trade-off between using ensemble techniques, like XGBoost, and single models, like logistic regression, KNN, or SVM, is valid. While ensemble techniques often achieve better performance, single models can be more easily interpretable and have lower complexity. This is an important consideration for applications in which model interpretability is a priority, such as in medical or healthcare settings where the decisions made by the model may have a significant impact on patient outcomes. Regarding unsupervised learning methods, hierarchical clustering is a valid suggestion for clustering categorical data. Hierarchical clustering is a bottom-up approach that builds a hierarchy of clusters, with the option to stop at a particular level of granularity to obtain a desired number of clusters. This method can be particularly useful when the number of clusters is unknown or when the data is challenging to interpret. The paper \"Estimating the Optimal Number of Clusters in Categorical Data Clustering by Silhouette Coefficient\" is an excellent reference for the author to discuss in the context of unsupervised learning methods. The paper proposes a method for determining the optimal number of clusters in categorical data clustering based on the silhouette coefficient, which measures the quality of the clustering solution [28].