Advanced Machine Learning Techniques for Autism Spectrum Disorder Diagnosis: A Multimodal Data Fusion Approach

By creating a novel multimodal artificial intelligence framework that combines cutting-edge machine learning techniques with thorough data analysis across developmental stages, this research addresses significant shortcomings in traditional autism spectrum disorder (ASD) diagnostic methodologies. To analyze data from 750 participants across four developmental age groups spanning early childhood through adulthood, the study employs a systematic evaluation of eleven machine learning algorithms, including sophisticated deep learning architectures and more conventional methods like Naïve Bayes, Support Vector Machines, Logistic Regression, and K-Nearest Neighbors. To capture the complex neurological characteristics of ASD, the complete strategy combines multimodal data fusion techniques, including physiological signal processing, speech pattern recognition, facial expression analysis, and functional magnetic resonance imaging neuroimaging data. While Principal Component Analysis shows that three primary components account for 78% of dataset variance, advanced feature engineering techniques, such as denoising autoencoders that optimize 19,900 functional connectivity characteristics to 600 essential components, improve diagnostic precision. Stratified sampling and rigorous cross-validation procedures are used in the assessment framework to guarantee model reliability and demographic balance over a range of populations. Through creative multimodal data fusion and sophisticated pattern recognition skills, Convolutional Neural Networks reach remarkable diagnosis accuracy of 97.5%, much surpassing established approaches that show accuracy ranges from 70.5% to 84%. In a paradigm change toward objective data-driven ASD screening that finds subtle diagnostic signs often overlooked by traditional clinical evaluations, the study reveals age-specific neurological markers that show dynamic fluctuation throughout developmental stages. This technology development lays the groundwork for a more accurate and easily accessible diagnosis of autism spectrum disease by facilitating early intervention possibilities, individualized treatment plans, and enhanced comprehension of individual neurological variances.