International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Autism Spectrum Disorder Detection in Children using the Efficacy of Machine Learning Approaches

  • Tariq Rafiq (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus) ;
  • Zafar Iqbal (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus) ;
  • Tahreem Saeed (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus) ;
  • Yawar Abbas Abid (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus) ;
  • Muneeb Tariq (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus) ;
  • Urooj Majeed (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus) ;
  • Akasha (Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus)
  • 투고 : 2023.04.05
  • 발행 : 2023.04.30
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초록

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 Naïve 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.

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참고문헌

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