Machine Learning Techniques for Early Diagnosis of Autism Spectrum Disorder

Registration ID: IJNRD_304422

Published ID: IJNRD2503139

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Kalash Shetty, Shravan Kamat, Poonam Jain, Dr. Santosh Singh

Autism Spectrum Disorder (ASD) Neurodevelopmental condition Early diagnosis Timely intervention Machine learning techniques Toddler Autism dataset Kaggle Diagnostic questionnaire Artificial Neural Networks (ANN) k-Nearest Neighbours (kNN) Data pre-processing Label encoding Standardization Synthetic Minority Oversampling Technique (SMOTE) Class imbalance Model performance metrics Accuracy Precision Recall F1 score Confusion matrices Cross-validation Overfitting k-fold cross-validation Early ASD detection Clinical settings Community-based settings Feature engineering Generalizability Larger datasets

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that significantly impacts communication and gestures, pressing the significance of early diagnosis for timely intervention. This study employs machine learning techniques to identify ASD traits in young children and toddlers, using the Toddler Autism dataset obtained from Kaggle in July 2018. The dataset includes 1054 samples, with features derived from diagnostic questionnaire responses. Due to limited locally available data, external sources were incorporated to enhance model training. The research employs Artificial Neural Networks (ANN) and k-Nearest Neighbours (kNN) for classification objectives. Both models underwent data pre-processing, which included label encoding for categorical variables, standardization of numerical features, and the Synthetic Minority Oversampling Technique (SMOTE) to manage class imbalances. The assessment of model performance was performed using various well-known metrics such as accuracy, precision, recall, F1 score, and confusion matrices. The ANN achieved high accuracy with cross-validation results of 99.81%, demonstrating robust learning and minimal overfitting. The kNN model performed comparably, with test accuracy and cross-validation accuracy of 94.86% and 94.93%, respectively. Additionally, k-fold cross-validation confirmed the models' stability across multiple splits, with ANN and kNN exhibiting consistent performance. Results highlight the potential of these models in early ASD detection, emphasizing their utility in clinical and community-based settings. Future work will explore larger datasets and advanced feature engineering to enhance generalizability.

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Kalash Shetty, Shravan Kamat, Poonam Jain, & Dr. Santosh Singh (March-2025). Machine Learning Techniques for Early Diagnosis of Autism Spectrum Disorder. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), b329-b341. https://ijnrd.org/papers/IJNRD2503139.pdf

Volume 10 Issue 3, March-2025

Pages : b329-b341

Paper Reg. ID: IJNRD_304422

Published Paper Id: IJNRD2503139

Research Area: Science and Technology

Author Type: Indian Author

Country: Mumbai, Maharashtra, India

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