## 4 Results
### Classification Techniques and Performance Evaluation  
AI-driven classification techniques for detecting ASD are summarized in this section, with a focus on comparing their performance in terms of diagnostic accuracy, sensitivity, and specificity. Utilizing innovative feature selection and machine learning models, these approaches demonstrate the potential of AI to streamline diagnostics, facilitate early detection, and improve accessibility across various age groups and clinical settings. The study by [PERSON] and [PERSON] (2023) [41] sought to improve ASD screening accuracy by leveraging feature selection techniques alongside advanced machine learning classifiers. Their study involved multiple datasets covering toddlers, children, teens, and adults, allowing for a comprehensive evaluation of ASD traits across age groups. The researchers applied a multilayer perceptron (MLP) classifier, which is a type of artificial neural network commonly used in machine learning for classification and regression tasks. They applied it to formerly stated datasets and found that it outperformed other classifiers, achieving near-perfect accuracy in distinguishing between ASD and non-ASD individuals. To enhance model precision while minimizing feature complexity, they used Relief F, a feature ranking method, to select the most relevant predictors. This optimization allowed the MLP model to reach a peak accuracy of 93.6%, with high sensitivity and specificity rates, making it a robust tool for ASD detection. The study concluded that MLP's ability to accurately and efficiently classify ASD suggests it is a valuable screening tool adaptable for diverse age groups, supporting both clinical assessments and large-scale screenings. Building on the efficiency of AI in ASD diagnostics, [PERSON] et al. (2012) [36] explored reducing the length of the widely used Autism Diagnostic Interview-Revised (ADI-R), which originally includes 93 questions, by applying machine learning algorithms. Focusing on the Alternating Decision Tree (ADTree) classifier, the researchers aimed to identify a minimal yet effective subset of questions to streamline the diagnostic process. Remarkably, the ADTree classifier achieved over 99% accuracy using only seven questions, retaining high sensitivity and specificity. This condensed assessment enabled accurate ASD identification in children as young as 13 months, demonstrating the potential of AI-driven feature reduction to reduce diagnostic time without sacrificing quality. The study concluded that by streamlining the ADI-R, AI can facilitate quicker, more accessible screenings, offering a powerful tool for clinicians and caregivers seeking efficient and accurate assessments. The study by [PERSON] et al. (2024) [49] presents an innovative two-phase approach for addressing the variability in autism spectrum disorder (ASD) traits through machine learning techniques (data: behavioral, verbal, and physical). The first phase focuses on ASD identification, employing feature engineering techniques and machine learning models, such as a logistic regression (LR) and support vector machine (SVM) ensemble, achieving a classification accuracy of 94% using Chi-square extracted features. The second phase targets the development of personalized educational strategies by assessing behavioral, verbal, and physical responses, achieving a remarkable 99.29% accuracy in matching teaching methods to individual needs. This two-phase strategy highlights the potential of ML to enhance ASD diagnosis and intervention, providing tailored solutions that account for the distinct traits of each individual. The superior performance of the proposed LR-SVM ensemble underscores its effectiveness in addressing the challenges of ASD diagnosis and personalized intervention. [PERSON] et al. (2024) explored the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD) through a combined approach using machine learning (ML), deep learning (DL), and natural language processing (NLP).