## 4 Discussion  
In this study, Sudoscan was used in conjunction with AI algorithms to predict DAN, obtaining promising results. Some studies have used traditional clinical methods and biomarkers to assess the risk of neuropathy in diabetic patients [49]. These methods are valuable but often fall short of the accuracy and predictive power offered by advanced ML algorithms. ML has been applied in various studies to predict some diabetic complications such as retinopathy, nephropathy, and cardiovascular diseases. However, research specifically focused on autonomic neuropathy remains very poorly explored. [PERSON] et al. [50] demonstrated that ML could enhance the predictive accuracy to 85% for diabetic nephropathy. Our study reinforces this finding, achieving high predictive accuracy with LR (92.6%) and RF (96.3%) classifiers and also with Lazy classifiers (97% with six models). The advantages of our results are mainly the improved predictive accuracy compared to other studies, and the fact that Sudoscan, a non-invasive, fast, and friendly method, is used to assess the risk of neuropathy as opposed to other methods, such as nerve conduction or skin biopsies, could lead to the application of this method and their use in daily medical care. Integrating AI with Sudoscan data could lead to the development of automated real-time risk assessment tools. This is particularly beneficial in primary care settings where access to specialized diagnostic equipment may be limited. Our models could also be incorporated into electronic health record systems by providing alerts and recommendations to healthcare providers, enabling prompt interventions. Traditional assessments for DAN, such as nerve conduction studies, are effective but invasive, costly both physically and materially, and not suitable for frequent monitoring. Our approach using Sudoscan and AI offers a less expensive alternative while maintaining high accuracy. Biochemical markers such as HbA1c, cholesterol, and triglycerides are useful for monitoring diabetes but these do not provide specific information about autonomic neuropathy. Combining these markers with Sudoscan data in our predictive models improves the overall assessment. The study by [PERSON] and [PERSON]. [13], which involved 1275 subjects, is the only one that we could find that was comparable to ours and that attempts to identify early DAN in patients with diabetes using databases incorporating Sudoscan values. The machine learning models used in their study achieved a maximum accuracy of 82.82% with TabNet. By contrast, our study's findings, across all examined models, are noticeably superior. Specifically, our study obtained an accuracy of 92.59% with LR, compared to 80.47% in the comparative study. Similarly, the RF model in our study obtained an accuracy of 96.30%, substantially higher than the 80.78% reported in the comparative study. These outcomes demonstrate how well the models we employed in our study performed. Most likely, the diversity of clinical data from our study is the primary element enhancing the performance of tested models. In contrast, the comparative study has less clinical detail despite having a larger dataset. Comprehensive clinical metrics, including blood test results (cholesterol, triglycerides, creatinine, and HbA1C), are included in our collection. The enhanced prediction performance of our models was probably influenced by the addition of these important medical criteria. These findings suggest that the prediction models tend to perform better not only with a larger number of samples but also with more detailed and medically relevant training data. This study demonstrates promising results, but it is essential to validate these results in larger and more diverse populations to ensure method validation. Future research will focus on exploring and integrating additional variables and data sources to further improve predictive accuracy.