## 4 Related Work in the ML and DL Domain
### Questionnaire-Based Diagnosis of ASD
Their study carefully cleans data and removes unnecessary features to preprocess ASD datasets. It then uses a wide range of 27 classification methods, evaluating each one using 10-fold cross-validationand evaluating the results according to accuracy and F1-score. The focus is on feature engineering, which identifies the most important features for the best classifier performance by rating them using five benchmark methodologies. The results highlight the multilayer perceptron (MLP) as the best classifier and the'relief F' approach as the most successful feature selection technique for ASD identification among all age groups. [PERSON] et al. [68] developed an ML model that enabled more accurate identification of ASD in children, adolescents, adults, and toddlers. Their approach selected features using various methods and evaluated many classifiers using metrics, including AUROC, kappa statistics, F1-measure, and overall prediction accuracy. SVM consistently performed better in every age group than other classifiers. Significant ASD-related characteristics were found by analyzing several feature subsets employing the Shapley Additive Explanations (SHAP) approach. The data gathering process entailed using the autism screening app ASDTests, which assessed the risk of ASD in various age groups using the Q-CHAT-10 and AQ-10 questionnaires. Each age group was represented by a dataset with 18 to 23 characteristics. That same year, [PERSON] and [PERSON] [69] looked at the automated generation of an autism diagnosis tool using ML using a dataset of 701 samples that include individual characteristics and AQ-10-Adult data. Recursive Feature Elimination (RFE) was used for feature selection in the training and testing of ML classifiers, including ANN, SVM, and RF, on a preprocessed dataset. The ASD screening data collection for adults includes 701 samples from 67 nations, with 512 negative and 189 positive cases. The gender distribution of the samples is also balanced. Regarding age, gender, jaundice, and ASD, including app usage, missing data are added, and classifiers like SVM, RF, and decision tree (DT), including LR with RFE, are used. A total of 12 classification models were created. More recently, [PERSON] et al. [70] presented a federated learning (FL) method that uses locally trained SVM classifiers and LR classifiers to diagnose ASD in children and adults. Four datasets totaling more than 600 records each were considered to extract the features. The suggested strategy consists of five essential parts: gathering datasets, preprocessing data, locally training ML models, and testing to identify the best diagnostic model for ASD. Getting publicly accessible datasets, preprocessing and normalizing the data, and then individually implementing SVM and LR classifiers onto the dataset were the steps in the procedure. These classifiers then send their results across a centralized server, whereby a meta-classifier is developed to determine the best approach. Moreover, [PERSON] and [PERSON] [71] employed four non-clinical ASD screening datasets from Kaggle and the UCI ML library. These datasets comprise the following age groups: adults (704 cases, 21 features), adolescents (104 cases, 21 features), toddlers (1054 instances, 19 characteristics), and children (292 cases, 21 features). Several supervised ML models are used in the study, including RF, DT, SVM, kNN, NB, and LR. The study found that the best classifiers were DT, LR, and RF. Finally, we can mention the works by [PERSON] et al. [72] and [PERSON] et al. [73]. [PERSON] et al. [72] relied on a Long Short-Term Memory (LSTM) model to analyze parent-child conversations to identify indications of ASD in children. The data, which focused on improving speech, conduct, and communication, were gathered from various social media sites and groups that support kids with special needs. The gathered conversations underwent preprocessing. Then, the LSTM model was trained to identify sentiment patterns within the parent chats.