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@article{173978,
        author = {S. Boomika and V. Harini and K. Dharani and A. Saranyadevi},
        title = {Predictive Models For Autism Across Lifespans},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2433-2438},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173978},
        abstract = {autism spectrum disorder (ASD) is a neurodevelopmental condition marked by challenges in social interaction, communication and repetitive behaviors. Early detection and intervention are crucial for enhancing the quality of life for individuals with ASD. This study proposes a machine learning-based approach to predict autism risk across various age groups, including toddlers, children, adolescents and adults. By leveraging publicly available datasets, models such as Support Vector Machines (SVM), Random Forests and Neural Networks will be trained to achieve high accuracy and reliability in predictions. The system aims to identify patterns and markers indicative of ASD within each age group, thereby offering a scalable, cost-effective and accessible solution. This approach seeks to bridge the gap in autism detection, paving the way for early intervention and improved outcomes for individuals across all age groups},
        keywords = {},
        month = {March},
        }