# Machine Learning for Predicting Neurodevelopmental Disorders in Children
## 3 Experimental Results and Discussion  
For an exhaustive evaluation of the model's performance, we incorporated an analysis of the confusion matrix and receiver operating characteristic (ROC) curves. The confusion matrix elucidated a substantial concordance between the model's prognostications and the actual classifications: 81.2% of cases without NDs (class 0) were correctly identified (true negatives), while 85.7% of cases with NDs (class 1) were accurately classified (true positives). Conversely, 18.8% of cases were erroneously classified as non-NDs (false positives), and 14.3% of non-ND cases were misclassified as NDs (false negatives), as delineated in Figure 6.  
Furthermore, the ROC curves for both categories (absence and presence of NDs) were incorporated to furnish a visual elucidation of the model's discriminative capacity. These curves exhibit pronounced class separation, with elevated area under the curve (AUC) values signifying the model's robust capability to correctly classify cases based on the presence of NDs (Figure 7). The inclusion of these visual and statistical analyses not only augments our comprehension of the model's performance but also lays a substantial groundwork for subsequent inquiries and practical implementations in our study. We introduced an analytical extension to the logistic regression model for predicting NDs, embodied in the integration of a sophisticated nomogram. This tool, fundamental in the interpretation of predictive models, transforms complex results into an intuitive visual representation, offering a quantitative and qualitative understanding of the impact of each predictive variable. The nomogram, built on a foundation of carefully calibrated parameters, illuminates the probability of absence (target class 0) and presence (target class 1) of NDs with extraordinary precision. For the absence of NDs, a total probability of 81% and a log-odds ratio of 5.79 are manifested through a mosaic of attributes: \"Language Proficiency and Social\" (2.64 points, value 0.9), \"Text Comprehension and Processing\" (0.6 points, value \(-\)0.7), \"Cognitive Precision\" (1.38 points, value 0.4), \"Auditory Processing Skills\" (1.13 points, value 0.4), and \"Cognitive Communication Skills\" (0.05 points, value 0.1). In contrast, the presence of NDs,  
Figure 5: Descriptive statistics of 184 participants. Figure 6: Confusion matrix. with a total probability of 19% and a log-odds ratio of 5.05, is influenced differently by these same attributes, reflecting the complex nature of neurodevelopmental disorders. The detailed analysis of the nomogram, illustrated in Figure 8, reveals a dynamic and significant correlation between the variables and the probability of the presence of NDs. Each line in the nomogram, with its length and direction, not only indicates the importance and influence on the prediction but also tells a story of how the variables interact in a complex clinical context. The points assigned to each variable represent their relative weight in the model; higher values indicate a greater impact on the outcome probability. In particular, we observed that reducing the values of \"Language Proficiency and Social\", \"Text Comprehension and Processing\", \"Cognitive Precision\", and \"Auditory Processing Skills\", while increasing \"Cognitive Communication Skills\", results in a marked increase in the predicted probability of the presence of NDs. This observation underscores the critical importance and sensitivity of the selected variables in our model and opens new  
Figure 7: ROC curves for ND presence and absence. perspectives for future investigations, suggesting specific pathways through which targeted interventions could positively influence the incidence and management of NDs. The coefficients represent the effect of predictor variables on the log-odds of the binary target variable (ND). Let us see the interpretations for each coefficient (Figure 9).