Early Detection of Autism Spectrum Disorder (ASD) using Traditional Machine Learning Models

Autism Spectrum Disorder (ASD) is a mental disorder among children that is difficult to diagnose at an early age of a child. People with ASD have difficulty functioning in areas such as communication, social interaction, motor skills, and emotional regulation. They may also have difficulty processing sensory information and have difficulty understanding language, which can lead to further difficulty in socializing. Early detection can help with learning coping skills, communication strategies, and other interventions that can make it easier for them to interact with the world. This kind of disorder is not curable but it is possible to reduce the symptoms of ASD. The early age detection of ASD helps to start several therapies corresponding to ASD symptoms. The detection of ASD symptoms at an early age of a child is our main problem where traditional machine learning algorithms like Support Vector Machine, Logistic Regression, K-nearest neighbour, and Random Forest classifiers have been applied to parents’ dialog to understand the sentiment of each statement about their child. After completion of the prediction of these models, each positive ASD symptoms-related sentence has been used in the cosine similarity model for the detection of ASD problems. Samples of parents’ dialogs have been collected from social networks and special child training institutes. Data has been prepared according to the model for sentiment analysis. The accuracies of these proposed classifiers are 71%, 71%, 62%, and 69% percent according to the prepared data. Another dataset has been prepared where each sentence refers to a particular categorical ASD problem and that has been used in cosine similarity calculation for ASD problem detection.

@article{Mukherjee2023,
  title     = {Early Detection of Autism Spectrum Disorder (ASD) using Traditional Machine Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Prasenjit Mukherjee and Sourav Sadhukhan and Manish Godse and Baisakhi Chakraborty},
  doi       = {10.14569/IJACSA.2023.0140626},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140626}
}