## 3 Results
### Prediction Using Machine Learning Methods
The performance of the models is displayed together with balanced accuracy, ROU AU, and F1 score, as can be observed in Table 8.  
The LazzyClassifier method was also tested to see if there are other, better classifiers. It can be seen from Table 8 that very good performances were obtained with Linear SVC, Linear Discriminate Analysis, Calibrated Classifier CV, Ridge Classifier CV, Ridge-Classifier, Passive Aggressive Classifier, LGBM Classifier, and Extra Trees Classifier. Using these models, we obtained an accuracy of 96%, an F1 score of 0.96, and an ROC AUC value of 0.97 (except LGBM Classifier and Extra Trees Classifier, which had an ROC AUC of 0.94). These models have a very high ability to distinguish between positive and negative cases of diabetic autonomic neuropathy. Balanced accuracy (0.97) shows that these models perform well even on unbalanced datasets and the values obtained from the Sudoscan tests together with the other clinical and demographic data collected can be used to predict the diagnosis of diabetic autonomic neurotrophy with very good accuracy. A performance of 93% was obtained with the classifiers SGD Classifier, Perceptron, Logic Regression, Extra Tree Classifier, XGB Classifier, RandomForest Classifier, Ada Boost Classifier, Bagging Classifier, Nu SVC, and SVC. This accuracy of 93%, is a decent one, with a value for balanced accuracy and ROC AUC of 0.91-0.95 indicating acceptable \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Mean Accuracy** & **Mean ROC AUC** \\ \hline Logistic Regression & 95.6\% & 99.75\% \\ Random Forest & 99.23\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: K-fold cross-validation results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & **ROC AUC** & **F1 Score** \\ \hline Linear SVC & 0.96 & 0.97 & 0.97 & 0.96 \\ Linear Discriminate Analysis & 0.96 & 0.97 & 0.97 & 0.96 \\ Calibrated Classifier CV & 0.96 & 0.97 & 0.97 & 0.96 \\ Ridge Classifier CV & 0.96 & 0.97 & 0.97 & 0.96 \\ Ridge Classifier & 0.96 & 0.97 & 0.97 & 0.96 \\ Passive Aggressive Classifier & 0.96 & 0.97 & 0.97 & 0.96 \\ SGD Classifier & 0.93 & 0.95 & 0.95 & 0.93 \\ Perceptron & 0.93 & 0.95 & 0.95 & 0.93 \\ Logic Regression & 0.93 & 0.95 & 0.95 & 0.93 \\ LGBM Classifier & 0.96 & 0.94 & 0.94 & 0.96 \\ Extra Trees Classifier & 0.96 & 0.94 & 0.94 & 0.96 \\ Bernoulli NB & 0.89 & 0.92 & 0.92 & 0.89 \\ Decision Tree Classifier & 0.89 & 0.92 & 0.92 & 0.89 \\ Nearest Centroid & 0.89 & 0.91 & 0.91 & 0.89 \\ Extra Tree Classifier & 0.93 & 0.91 & 0.91 & 0.93 \\ XGB Classifier & 0.93 & 0.91 & 0.91 & 0.93 \\ Random Forest Classifier & 0.93 & 0.91 & 0.91 & 0.93 \\ Ada Boost Classifier & 0.93 & 0.91 & 0.91 & 0.93 \\ Bagging Classifier & 0.93 & 0.91 & 0.91 & 0.93 \\ Nu SVC & 0.93 & 0.88 & 0.88 & 0.92 \\ SVC & 0.93 & 0.88 & 0.88 & 0.92 \\ Gaussian NB & 0.81 & 0.87 & 0.87 & 0.82 \\ K-Neighbors Classifier & 0.89 & 0.85 & 0.85 & 0.89 \\ Quadratic Discriminant Analysis & 0.78 & 0.77 & 0.77 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results obtained with Lazzy Classifier. performance. The SVC and Nu SVC models had values of 0.88 for balanced accuracy and ROC AUC, suggesting a somewhat poorer performance in handling imbalances in the data. Moderate performance (89-81% accuracy) was obtained with the classifiers Bernoulli NB, Decision Tree Classifier, Nearest Centroid, Gaussian NB, and K-Neighbors Classifier. The ROC AUC and balanced accuracy of these models ranged between 81% and 89%.