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Autism Spectrum Disorder Detection in Children using the Efficacy of Machine Learning Approaches


Tariq Rafiq, Zafar Iqbal, Tahreem Saeed, Yawar Abbas Abid, Muneeb Tariq, Urooj Majeed, and Akasha


Vol. 23  No. 4  pp. 179-186


For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Na?ve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.


Autism Spectrum Disorder, KNN, Gaussian Na?ve Bayes, Random Forest, Multi-Layer Perceptron, Logistic Regression, Autism Spectrum Quotient