# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 3 Experimental Results and Discussion
### Experiments
These results indicate that the model has a good ability to discriminate between the classes, achieving a balance between precision and recall. Overall, the model appears to be effective in predicting the presence of NDs based on the provided features. Advantages and Limitations of Applying Machine Learning in the Study of Neurodevelopmental Disorders  
The adoption of machine learning in our study represents a data-driven approach that unveils patterns and correlations not immediately evident through traditional analytical methods. The ability to process and analyze large volumes of data allows the use of complex and detailed datasets that enhance the quality of predictions. For instance, machine learning has been instrumental in developing personalized assistive tools, demonstrating significant potential in enhancing the educational and social development of children with various neurodevelopmental disorders. This approach has shown promise in improving their social interaction and supportive education, indicating a promising direction for enhancing care and education in these areas [48]. Similarly, the investigation into the use of machine learning-based diagnostic techniques for the early prediction of neurodevelopmental disorders in children highlighted reduced intervention time and increased accuracy [15]. The ability to compare different models, such as logistic regression, random forest, and SVM, enables us to select the best performing and most suitable one for the specific context of the study. The use of stratified 10-fold cross-validation provides a reliable estimate of the model's performance and minimizes the risk of overfitting, while validation on an independent external cohort confirms the generalizability of the models to a broader population, increasing the robustness of the study. However, it is important to recognize the limitations associated with these methods. The complexity and interpretation of some machine learning models can be daunting, especially for those without specific training. Studies that have explored the use of machine learning to analyze qualitative data have indicated that, although it can provide valuable insights, it requires careful interpretation and validation [49]. The quality and reliability of predictions strongly depend on the quality of the input data; erroneous, incomplete, or biased data can lead to misleading results. Despite the use of cross-validation, there is always a risk of overfitting, especially when working with small datasets or a high number of features. Moreover, the literature analysis has highlighted how many machine learning models can be sensitive to imbalanced datasets, affecting their ability to generalize to new data [38]. Finally, the configuration, interpretation, and validation of models require in-depth knowledge and specific skills, which can limit accessibility for some research teams. Studies that have developed models to differentiate individuals with specific conditions have highlighted the complexity involved in developing and interpreting these models [50]. Figure 11: Model’s performance indicators. In conclusion, while the machine learning-based approach offers numerous advantages in terms of analytical capacity and data processing, a deep understanding of its limitations and challenges is essential to ensure that the results are interpreted correctly and used responsibly in a clinical context. This balance between the advantages and limitations of machine learning guides our study towards a rigorous and informed scientific investigation, aiming to provide more effective and reliable tools for the diagnosis and treatment of neurodevelopmental disorders.