# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 3 Experimental Results and Discussion
### Experiments  
At the heart of our scientific investigation into neurodevelopmental disorders in children, we have adopted a rigorous and cutting-edge methodological approach to select the most effective predictive model. Figure 10 represents the culmination of this analytical process, offering a detailed report of the metrics for all considered regressors. This visualization not only embodies our dedication to scientific precision but also serves as a critical reference point for understanding the comparative performance of various models. Figure 8: Nomogram of variables affecting ND probability. Figure 10 illustrates the performance of models such as logistic regression, random forest, gradient boosting, SVM, kNN, naive Bayes, stochastic gradient descent (SGD), and AdaBoost, evaluated through rigorous metrics such as area under the ROC curve (AUC), accuracy (CA), F1 score (F1), precision (Prec), recall, and Matthews correlation coefficient (MCC). These metrics have been carefully selected to provide a holistic and multidimensional evaluation of each model's capabilities, ensuring that our final choice is informed by a complete understanding of their performance. Logistic regression emerges as a beacon of excellence in this analytical landscape, distinguishing itself for its superior discrimination ability and an optimal balance between precision and sensitivity. With an AUC of 0.730, accuracy of 0.815, F1 score of 0.776, precision of 0.823, and recall of 0.815, this model not only demonstrates an excellent ability to distinguish between classes but also provides a clear and direct interpretation of its predictions, a crucial aspect for clinical application. External validation on an independent cohort has further strengthened our confidence in logistic regression, confirming its robustness and applicability to a broader and more heterogeneous population. This step was crucial to ensure that the selected model is not only accurate and reliable on the training dataset but also generalizable and applicable in real-world scenarios. Despite a slight decrease in performance with an AUC of 0.729, accuracy of 0.725, F1 score of 0.686, precision of 0.703, and recall of 0.725, the model maintains a strong discriminative ability and a good balance between key metrics. The AUC curves presented in Figure 10 offer an immediate visual comparison of each model's ability to balance true positives and false positives, further highlighting the superiority of logistic regression in the context of our evaluation criteria. This visual comparison not only confirms our choice but also provides a transparent and quantifiable basis for the decision, ensuring that the selected model is the most suitable to guide informed and accurate clinical decisions. The performance indicators of the predictive model, using a logistic regression algorithm to forecast the presence of NDs, are described in Figure 11.  
This indicates that the model is achieving a good balance between accuracy, precision, and recall. The model was evaluated using stratified 10-fold cross-validation. An AUC of 0.7229 suggests that the model has a reasonably good discriminative capacity between the two classes (presence or absence of NDS), while an accuracy of 0.8152 indicates that approximately 81.52% of instances are correctly classified by the model. The F1-score, which considers both precision and recall, is also quite high with a value of 0.7763, indicating that the model is performing well in identifying true positive instances while minimizing false positives and false negatives. With a precision value of 0.822616, the model has a good proportion of correct positive predictions compared to all positive  
Figure 9: Model coefficients. predictions. This indicates that when the model predicts that an instance belongs to the positive class, it is correct about 82.26% of the time. Figure 10: Comparative ROC curves and performance parameters of machine learning models.