# Machine Learning for Predicting Neurodevelopmental Disorders in Children
###### Abstract
These algorithms can identify patterns and connections within the data that may be difficult for humans to comprehend, laying the groundwork for more accurate and efficient diagnostic tools to assist clinicians' early detection and supporting children with NDs in a critical period where early intervention can significantly impact long-term outcomes [18; 22; 23; 24]. Numerous studies have been conducted on various types of NDs, using a wide range of ML techniques for diverse sorts of data for diagnostic and prediction processes, employing efficient and sophisticated standards to attain accuracy and cost-effectiveness [6; 15; 25; 26]. These ML techniques mainly employ supervised (like regression, support vector machines (SVMs), decision trees, artificial neural networks (ANNs), and Bayesian logic), and unsupervised learning methods (like clustering, association rules, and dimensionality reduction) [27]. Semi-supervised learning and reinforcement learning techniques are used less frequently [27]. The current literature reports ML classifiers used to diagnose ASD, developmental language disorder (DLD), and global developmental delay (GDD) in preschoolers [24]. To be precise, different models were utilized, including neural networks, decision trees, support vector machines, XGBoost (eXtreme Gradient Boosting), and logistic regression, while accuracy was evaluated by twelve doctors. The study reported the potential for improving our understanding of these disorders' diagnosis based on behavioral and developmental features. In a different study, reduced motor synergies can lead to motor control issues in individuals with ASD, and an early detection of motor impairments has been suggested that can effectively differentiate between ASD and typically developed (TD) individuals using a SVM with a radial basis function (RBF) [28]. In another research study, a face recognition framework to detect early signs of ASD analyzed facial traits and eye contact using k-means clustering; unsupervised learning has been suggested to assist medical professionals accurate clinical decisions [29]. Also using the k-means algorithm, [PERSON] et al., studied ASD and found three distinct categories of children with ASD [30]. They also found that developmental delay, gastrointestinal issues, and immunological imbalances are common comorbidities associated with ASD. These findings can help identify comorbiditiesand subgroups within the ASD population. Moreover, ML-based evaluation was proposed to detect heart rate variability in children with ASD dealing with bradycardia (low heart rates) [31]. Most cardiovascular conditions in children with ASD are congenital. Other researchers used eye-tracking data in ML to screen for ASD [32], and their neural network model outperformed traditional methods, indicating that eye-tracking data could help doctors to quickly and accurately identify autism. Early indicators of ADHD and ASD have been reported with ML and deep learning (DL) approaches, utilizing convolutional neural networks (CNNs) and deep learning APIs [15]. DL techniques and CNNs have utilized personalized spatial-frequency anomalies in EEG power spectrum density to identify ADHD in children [33]. Also, a CNN have been used to distinguish ADHD from healthy controls by using stacked multi-channel EEG time-frequency decompositions [34]. ML prediction with EEG signals' morphological and feature extraction techniques were analyzed employing the Bernoulli naive Bayes classifier, and outperformed others in the distinction of ADHD [35]. Researchers have developed a new method to categorize adolescents with intellectual impairments, involving the extraction of speech features from linear predictive coding (LPC)-based cepstral parameters and mel-frequency cepstral coefficients (MFCC) [36].