## 3 Results
### Prediction Using Machine Learning Methods
The model is trained on the training dataset using fit(). Then, prediction is performed on the test dataset using predict(). In the last step, the models are evaluated. Table 6 presents the performance metrics obtained with the LR and RF classifiers. We can see that RF outperformed the LR classifier with an accuracy of 96.30%, compared to 92.59%. These findings imply that the models are trustworthy in this particular DAN prediction. The RF model yielded a ROC AUC score of 95.83%, whereas the LR model yielded a score of 92.50%. These results show that both models can accurately differentiate between positive and negative cases of diabetic autonomic neuropathy. With the RF model, we obtained an F1 score of 96.00%, whereas the LR model has a score of 92.31%. This suggests that both models can accurately predict both classes, but the RF model performs better once again. The sensitivity for both models was 91.67%. This indicates that 91.67% of individuals at risk were correctly identified by both models as positive cases of DAN. The RF model achieved a flawless specificity of 100%, while the LR model's specificity was 93.33%. Specificity shows how well the classifier recognizes negative cases. The RF perfect score shows that it accurately detected every negative occurrence. The confusion matrices demonstrate that both models generate relatively few errors; however, the RF model performed better because it produced no false positives. Upon first evaluating our models, we found that RF produced 96% accuracy. Nonetheless, there may be bias introduced when evaluating the model with a single test fraction of 20%. In particular, the claimed accuracy may be too optimistic if the test set included softer cases. On the other hand, accuracy can be overestimated if the test set is extremely challenging. This fluctuation emphasizes the danger that comes with depending just on one random split of the data set. For the two classification models, we used k-fold cross-validation in order to solve this issue and obtain a more trustworthy model performance metric. The dataset is split up into 10 subsets (folds) for k-fold cross-validation. To make sure that every data point is used for both training and testing, each model is trained on nine folds and tested on the remaining fold. This process is repeated 10 times. By using this method, the likelihood of bias is decreased, and a more precise and broadly applicable estimate of the model's performance is obtained. K-fold cross-validation makes sure that the reported performance metrics, including accuracy and ROC AUC, reflect the model's capacity to generalize to new data rather than relying on the features of a particular subset by employing the complete data set for evaluation in a methodical manner. The results of the K-fold cross-validation, reported in Table 7, show an average accuracy of 95.6% and an average ROC AUC of 99.75% obtained with RL, indicating strong performance and excellent class discrimination, while, with RF, we obtained an average accuracy of 99.23% and a perfect average ROC AUC of 100%, demonstrating extremely high accuracy and perfect class separation. The results show that both models are highly efficient, with RF slightly outperforming RL. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **ROC AUC** & **F1 Score** & **Sensitivity** & **Specificity** & **Confusion Matrix** \\ \hline Logistic Regression & 92.59\% & 92.50\% & 92.31\% & 91.67\% & 93.33\% & [[14; 1], [1; 11]] \\ Random Forest & 96.30\% & 95.83\% & 96.00\% & 91.67\% & 100\% & [[15; 0], [1; 11]] \\ \hline \hline \end{tabular} ROC AUC—Receiver Operating Characteristic Area Under the Curve.  
\end{table} 
Table 6: Results obtained with Logistic Regression and Random Forest. Using the same training and testing dataset created initially, LazyClassifier is applied. The first step of the Python code is the initialization of the classifier with the default parameters, then the classifier trains multiple models on the training data and makes predictions on the testing data.