## 4 Related Work in the ML and DL Domain  
### Questionnaire-Based Diagnosis of ASD  
Early screening is crucial for timely intervention in children's development. Regular screening by using tools like the Autism Spectrum Quotient (AQ), the Social Communication Questionnaire (SCQ), and the Modified Checklist for Autism in Toddlers (M-CHAT) helps gather information about a child's social skills and behavior. This information aids healthcare professionals in diagnosing ASD, a process they can only carry out. Diagnosis involves the use of standardized diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview-Revised (ADI-R), and the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). These methods rely primarily on interviews, which are easily adaptable to participants of all ages [61]. Early identification helps to ensure prompt intervention, which has a significant impact on the management of ASD. They function similarly to checklists physicians use to spot trends that may indicate ASD and order early screens and therapies. The diagnosis of ASD is undergoing a revolution due to recent developments in AI. For example, ML and DL algorithms process through enormous volumes of data from many sources. This data consists of medical records, questionnaires, and behavioral assessments. By examining these many sources, AI can find trends and indicators related to ASD [62,63]. The following lines describe, chronologically, relevant works where AI has interacted with data gathered from questionnaires to improve ASD diagnosis. In 2019, [PERSON] and [PERSON] [64] proposed a classification system for ASD data to facilitate early ASD diagnosis using practical algorithms. It used three ASD datasets to consider kids, teens, and adults (AQ-10-Child, AQ-10-Adolescence, and AQ-10-Adult). Their research used Random Forests (RF), SVM, and \(k\)-nearest neighbors (kNN) for classification in datasets with 20 variables that include screening questions and personal information. Their study suggests that RF and SVM are valuable techniques for classifying ASD. Also in 2019, [PERSON] et al. [65] used datasets that included people with ASD of all ages, from toddlers to adults. It used a variety of approaches, such as sine function, logarithmic, and \(Z\)-score normalization, to modify the characteristics. They used the adjusted datasets to evaluate a variety of ML-based classifiers. Feature selection techniques were then used to \(Z\)-score normalized datasets to uncover significant ASD risk variables across age groups. The study showed how well optimal ML techniques can predict ASD, even in the face of obstacles like missing values and data noise. The UCI ML archives and Kaggle datasets included records for 1054 toddlers, 248 children, 98 adolescents, and 609 adults, with varying gender proportions for each age group. A year later, [PERSON] and [PERSON] [66] used three publicly accessible non-clinical ASD datasets to predict and analyze ASD across various age groups: one with 21 characteristics for children (292 occurrences), one for adults (704 instances), and one for adolescents (104 instances). Their technique included a train-test split of 80:20 and tested NB, SVM, LR, kNN, neural networks (NN), and CNN models. CNN models performed better across all datasets. Later, the study conducted by [PERSON] et al. [67] focused on increasing the accuracy and swiftness of ASD diagnosis by integrating ML approaches with conventional methodologies. The dataset had 1054 instances at first, with 18 features. However, unimportant and categorical attributes had to be removed during the preprocessing. The dataset was then divided into 80% for training (843 samples) and 20% for testing (211 samples). Another important work is that of [PERSON] et al. [22], who used various classification algorithms on datasets with different age groups (including toddlers, children, adolescents, and adults) to explore how to automate the diagnosis of ASD.