# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
## 2 Materials and Methods  
### Related Works  
[PERSON] et al. (2022) [9] conducted a study on using machine-learning techniques along with social visual attention for assessing autism in children. The study incorporates biomarkers such as eye movements toward social stimuli for facilitating early diagnosis. Other body movements, neural correlates, electrodermal activities, and genomes may also be used as biomarkers. Statistical approaches like explanatory and predictive strategies may be used effectively to analyze autism in individuals. The study asserts that Support Vector Machines (SVM) and neural networks are excellent machine-learning tools for ASD classification. However, SVD performs better in terms of performance and cost-effectiveness. Similarly, [PERSON] et al. (2022) [10] suggested eye-tracking-based diagnosis using machine-learning models for detecting ASD. The study incorporates scanning the eyes' path to extract projection points for analyzing behavior among children. The analysis uses feed-forward networks (FFN), ANNs, and pre-trained CNNs using GoogleNet and ResNet-18. The study also introduces a hybrid model combining GoogleNet with SVMs and ResNet-18 with SVMs. The overall accuracy achieved due to these hybrid models is 95.5% and 94.5%, respectively. [PERSON] et al. (2022) [11] presented yet another interesting study using voice markers with machine learning to detect autism in individuals. The analysis incorporates novel cross-linguistic datasets along with a pipeline for minimizing overfitting. Multiple analyses are conducted on the participants in terms of tasks and languages. MI models are observed to be efficient in identifying autism from voices. The study's main aim is to assess the generalizability of the different models, and it is observed that the models do not generalize well to different tasks and new languages. [PERSON] et al. (2021) [12] performedan extensive analysis of ASD using Applied behavior analysis (ABA). The gold standard treatment has been personalized using 29 participants for the clinical study. The patient similarity method and collaborative filtering techniques have been applied to the data. The study observed an average accuracy of 81-84%, followed by a normalized discounted cumulative gain of 79-81%. The efficiency of the models has been validated using the percentage of recommended goals, and the treatment recommendation is generalizable to other methods. [PERSON] et al. (2022) [10] analyzed ASD using a supervised machine-learning analytic approach. Prosodic differences in ASD are prevalently seen in multiple languages due to cross-linguistic variability. The analysis considers acoustic features and intonational aspects of prosody considering English and Cantonese languages. The ML models assert that efficient ASD diagnosis with respect to rhyme-relative features. However, the relevant intonational features did not show efficient classification for Cantonese. The study asserts that rhythm is a key prosodic feature, and intonation can lead to variation in other prosodic properties. [PERSON] et al. (2022) [13] proposed machine-learning techniques for identifying autism in children using physiological and behavioral data. Using feature extraction techniques on electroencephalography (EEG), eye fixation, and facial data, ASD can be detected in children with improved efficiency and reduced costs. The study uses a weighed naive Bayes classification technique for multimodal data fusion and achieves an accuracy of 87.50%. The study also incorporates confusion matrices and graphs to depict how eye movements, facial expressions, and EEG may have varying discriminative powers for ASD detection. [PERSON] et al. (2022) [14] suggested the application of ML algorithms to neuroimaging features from structural and functional Magnetic resonance imaging (MRI). This could be advantageous in comprehending brain alterations and characteristics of ASD.