## 3 Results
### Prediction Using Machine Learning Methods  
Codes were implemented in Phyton 3.9 using two different methods to classify the data: LR and RF. These methods are applied to the dataset collected from patients without neurotrophic treatment (135 subjects) ('Age', 'BMI', 'Avg feet', 'Avg hands', 'DM age', 'HbA1c', 'Cholesterol', 'Triglyceride', 'SBP', 'DBP', 'Creatinine') to predict the risk of diabetic autonomic neuropathy based on the target variable represented by the mean values obtained for hands and transformed into binary. This decision, to exclude patients with neurotrophic treatment, was made to ensure that the models were trained on data reflecting the risks of diabetic complications without the influence of treatment effects. We additionally employed the Lazzy Predict library to create various machine-learning methods. This machine-learning Python package allows us to rapidly determine which models, without manually configuring and testing each one, could work best for our dataset. Hundreds of models can be tested to find which one fits our dataset the best. With its display of multiple metrics like accuracy, F1 score, and ROC AUC, Lazzy Predict provides a clear picture of the performance of several models. Explanation of the stages of the used code:  
1. The first classification model used is a linear one: Logistic Regression. Then was applied Random Forest, a model that uses multiple decision trees to improve classification performance. To evaluate the performance of the classifiers we used the following: confusion matrix, roc_auc_score, and accuracy_score. The confusion matrix gives a complete overview of the classifier performance by comparing predictions with actual values, and the accuracy_score quantifies the percentage of correct predictions. Roc_auc_score assesses the model's ability to correctly classify positive and negative predictions at different thresholds. 2. Preparation of data for classification: all collected values ('Age', 'BMI', 'Avg feet', 'Avg hands', 'DM age', 'HbA1c', 'Cholesterol', 'Triglyceride', 'SBP', 'DBP', 'Creatinine') are assigned X, and the column 'Hands' (average for ESC values measured for the left and right hand--binary: 0--representing the class at risk or possible risk of DAN (78 samples), 1--representing the class at no risk of DAN (57 samples)), the variable Y. 3. Data are split into the training set (80%) and the test set (20%), using the train_test_split function. 4. The models are initialized with the specified parameters using the two classifiers: LR and Random Forest. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Hands**} & \multicolumn{4}{c}{**Feet**} \\  & **r** & **_p_** & **95\% CI** & **r** & **_p_** & **95\% CI** \\ \hline Age & \(-0.14\) & \(0.073\) & \([-0.65,-0.23]\) & \(-0.09\) & \(0.224\) & \([-0.66,-0.24]\) \\ BMI & \(-0.20\) & \(0.009\) & \([-0.35,-0.05]\) & \(-0.31\) & \(<\)0.001 & \([-0.45,-0.17]\) \\ Neuro risk & \(-0.89\) & \(<\)0.001 & \([-0.95,-0.83]\) & \(-0.89\) & \(<\)0.001 & \([-0.95,-0.83]\) \\ Cardio risk & \(-0.08\) & \(0.305\) & \([-0.23,0.07]\) & \(-0.23\) & \(0.003\) & \([-0.37,-0.09]\) \\ Nefro risk & \(-0.24\) & \(0.001\) & \([-0.38,-0.10]\) & \(-0.30\) & \(<\)0.001 & \([-0.44,-0.16]\) \\ DM age & \(-0.23\) & \(0.003\) & \([-0.50,-0.16]\) & \(-0.15\) & \(0.047\) & \([-0.52,-0.18]\) \\ HbA1C & \(0.04\) & \(0.613\) & \([-0.13,0.21]\) & \(-0.02\) & \(0.809\) & \([-0.19,0.15]\) \\ Cholesterol & \(-0.31\) & \(<\)0.001 & \([-0.15,0.19]\) & \(-0.48\) & \(<\)0.001 & \([-0.46,0.16]\) \\ Triglyceride & \(-0.30\) & \(0.001\) & \([-0.34,-0.04]\) & \(-0.26\) & \(0.001\) & \([-0.37,-0.07]\) \\ SBP & \(-0.04\) & \(0.573\) & \([-0.21,0.13]\) & \(-0.17\) & \(0.028\) & \([-0.32,-0.02]\) \\ DBP & \(-0.01\) & \(0.947\) & \([-0.18,0.16]\) & \(-0.04\) & \(0.588\) & \([-0.21,0.13]\) \\ Creatinine & \(-0.02\) & \(0.747\) & \([-0.19,0.15]\) & \(0.04\) & \(0.568\) & \([-0.13,0.21]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Spearman correlations and significance level.