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@article{174272,
        author = {Borra Baby Sravanthi and Chalapati Teja Sri and Swapna Duggi and Kurapati Prudhvi Raj and DR.D. ANUSHA},
        title = {DEEP ANALYSIS OF AUTISM SPECTRUM DISORDER DETECTION TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3852-3858},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174272},
        abstract = {Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition affecting communication, behavior, and social interactions. Early detection and intervention are critical for improving outcomes, and advancements in artificial intelligence (AI) and machine learning (ML) present promising opportunities for early diagnosis. Various ML algorithms, including XGBClassifier, Random Forest, Decision Trees, AdaBoostClassifier, KMeans, Extra Tree Classifier, and Artificial Neural Networks (ANN), have been explored for ASD prediction. These algorithms can analyze large datasets, detect patterns, and make predictions with high accuracy. For instance, XGBClassifier offers efficient handling of complex data, while Random Forest mitigates overfitting. Decision Trees provide interpretability, and AdaBoostClassifier combines multiple weak classifiers for stronger predictive power. KMeans helps identify subgroups within the ASD population, while ANN models capture intricate data relationships. However, challenges such as data imbalance, model complexity, and computational requirements need to be addressed. Researchers must balance accuracy, efficiency, and interpretability when selecting the best model. With continuous refinement and validation using diverse datasets, ML algorithms can revolutionize early ASD detection, enabling more accurate, timely, and cost effective interventions that improve the lives of individuals with ASD.},
        keywords = {Machine Learning, XGBClassifier, Random Forest, Decision Trees, Extra Tree Classifier, AdaBoostClassifier, KMeans, and Artificial Neural Networks (ANN).},
        month = {March},
        }