# Machine Learning for Predicting Neurodevelopmental Disorders in Children
###### Abstract  
Developmental domains like physical, verbal, cognitive, and social-emotional skills are crucial for monitoring a child's growth. However, identifying neurodevelopmental deficiencies can be challenging due to the high level of variability and overlap. Early detection is essential, and digital procedures can assist in the process. This study leverages the current advances in artificial intelligence to address the prediction of neurodevelopmental disorders through a comprehensive machine learning approach. A novel and recently developed serious game dataset, collecting various data on children's speech and linguistic responses, was used. The initial dataset comprised 520 instances, reduced to 473 participants after rigorous data preprocessing. Cluster analysis revealed distinct patterns and structures in the data, while reliability analysis ensured measurement consistency. A robust prediction model was developed using logistic regression. Applied to a subset of 184 participants with an average age of 7 years, the model demonstrated high accuracy, precision, recall, and F1-score, effectively distinguishing between instances with and without neurodevelopmental disorders. In conclusion, this research highlights the effectiveness of the machine learning approach in diagnosing neurodevelopmental disorders based on cognitive features, and offers new opportunities for decision making, classification, and clinical assessment, paving the way for early and personalized interventions for at-risk individuals. neurodevelopmental disorders; screening; children; machine learning prediction; logistic regression model +
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fully participate in social and professional interactions and effective communication [2; 3; 4; 5; 6; 7; 8; 9; 10]. NDs affect more than 10% of people globally, triggering long-term consequences and a significant economic burden [11]. NDs include the following [1; 12; 13]:  
* Autism spectrum disorders (ASD), which are characterized by behavior and communication difficulties;
* Attention deficit hyperactivity disorder (ADHD), which is characterized by inattention, impulsivity, and hyperactivity;
* Intellectual disability (ID), indicated by cognitive impairments;
* Specific learning disorder (SLD), characterized by a deficiency in communication;
* Communication disorders (CDs), illustrated by persistent difficulties in language acquisition and usage. The field of neurodevelopmental disorders and their identification has become increasingly complex and challenging [14]. Identifying neurodevelopmental disorders poses significant challenges due to the complexity and variability of their symptoms and presentations [15; 16; 17]. While medical advancements have helped, inaccurate diagnosis and comorbidities make it hard to establish precise diagnostic boundaries [18]. Particularly, more than one-third of individuals with ASD exhibit symptoms that match criteria for various disorders, resulting in numerous possible diagnostic combinations. Further, traditional diagnostic methods often rely on subjective observations and lengthy assessments, leading to delayed or inaccurate diagnoses [3; 19; 20; 21]. Early detection is crucial as the developing brain is adaptable, allowing for the creation of compensation mechanisms. Rapid medical intervention can contribute to the reduction or mitigation of symptoms, ultimately enhancing the individual's overall quality of life [18]. With advancements in technology, specifically in the domain of machine learning (ML), researchers are exploring innovative ways to enhance the accuracy and efficiency of diagnosing the risk of NDs. The potential of ML algorithms to analyze large amounts of complex data is enormous [18].