Abstract
Children with Autism Spectrum Disorder (ASD) is increasing rapidly worldwide and is a major concern nowadays. Considering the growing number of children with disability, there is not enough school for the training of children diagnosed with ASD. However, schools providing the training, can not provide the best training due to a lack of skilled manpower and efficient knowledge. Besides, most schools do not properly follow the required protocols for the training program. One of the widely used protocols is the Assessment of Basic Language and Learning Skills (ABLLS). According to the ABLLS, for evaluating an individual, an Initial Assessment is made and then an Individual Education Plan (IEP) is created. The evaluation system is not automated. Nobody knows how much time is needed to determine the improvement of functional behavior. In this work, we have proposed a model using Multiple Linear Regression and KNN classification machine learning algorithms to determine the Autism level of a child from the initial assessment dataset. By analyzing the dataset, we have shown improvement in ASD-diagnosed children. We have run the K-means clustering algorithm to determine which group of children needs more support and which group of children needs less support. For the level detection part, our model has shown high accuracy for both real and synthetic datasets.
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Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
First of all, we would like to thank “Spectra School for Autism” and “Sheher Autism Center” for helping us with data collection. We want to thank the teachers of both institutions for guiding us with domain knowledge and setting the range values. The authors also thankfully acknowledge the support of the members of the IIUC Data Science Research Group.
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Khan, S.I., Shafee, R.A., Huda, R. et al. Predicting the level of autism and improvement rate from assessment dataset using machine learning techniques. Int. j. inf. tecnol. 15, 1647–1652 (2023). https://doi.org/10.1007/s41870-023-01212-y
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DOI: https://doi.org/10.1007/s41870-023-01212-y