Abstract:
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms that affect an individual's social interaction, communication, and behavior. Early diagnosis and intervention play a crucial role in improving the quality of life for individuals with ASD. This thesis presents a comprehensive investigation into the analysis and detection of ASD using advanced machine learning techniques. The primary objective of this research is to develop a robust and accurate ASD screening tool that can assist in early identification. To achieve this goal, we leverage machine learning algorithms and a diverse range of data sources, including behavioral assessments, clinical records, and demographic information. The research explores the application of supervised learning, feature selection, and data preprocessing techniques to enhance the performance of ASD detection models. This thesis also delves into the development of a prototype application that combines sophisticated machine learning models with an intuitive user interface. The application enables caregivers, educators, and healthcare professionals to conduct preliminary ASD assessments efficiently and receive real-time feedback. Furthermore, the study examines the significance of feature selection and engineering in improving the interpretability of ASD detection models. It explores the potential of neural networks, support vector machines, and other state-of-the-art algorithms in achieving high diagnostic accuracy. The findings presented in this thesis contribute to the growing body of research in the field of autism spectrum disorder detection. The research outcomes have practical implications for early intervention and support for individuals with ASD, ultimately promoting better outcomes and enhanced quality of life. In conclusion, this thesis serves as a valuable resource for researchers, healthcare professionals, and stakeholders in the field of ASD. It underscores the potential of machine learning techniques in advancing the accuracy and efficiency of ASD detection and sets the stage for further research and innovation in this critical area of healthcare.
