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Machine learning based autism screening tool—a modified approach

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Abstract

The primary goal of the present research work is to build machine learning based classification model for classifying an individual with different degrees of autism that can yield better performance than traditional autism assessment scale used in India. The study aimed to find how significant are the selected additional 6 attributes for better identification and understanding of Autism spectrum disorder (ASD). This work focuses on a unique screening tool developed for the Indian population –Indian Scale for assessment of Autism(ISAA) to build the classification model. For better performance few additional significant features are also considered following proper procedure. Selective machine learning models are built and finally performance evaluation is done on all classifier results. As an alternative approach, a binary classifier multi-layer perceptron (MLP) model has also been trained on both the data sets to distinguish between people with autism who need less attention and those who need more attention for assessment and further intervention. Overall performance of 3 best classifiers (KNN, Random Forest, SVM) suggests that after considering additional 6 attributes SVM produced better efficacy in identification of degree of autism over conventional ISAA parameters with higher weighted F1 score of 85.7% and accuracy of 90% respectively. It is also reconfirmed with the results of the MLP model. The basic model converges after 71 epochs with training accuracy 97.78% wherein the extended model converges after 61 epochs with training accuracy 98.89%. The best model functions better than conventional scale of assessment which also made the screening task unbiased, accurate and time efficient.

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Data availability

The primary datasets generated by the process of clinical evaluation of psychologists for the persons with autism during and/or analysed during the current study available in the figshare repository with restricted access. DOI:—https://doi.org/10.6084/m9.figshare.23689833. However, data may be made available to the reviewer as and when required.

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Acknowledgements

We would like to express our whole-hearted appreciation to all clinical investigators who assessed the students of Pradip Centre for Autism Management for autism and other participants too and contributed significantly in data collection procedure—without whom this research would have never been possible.

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Author notes

  1. Arpita Mazumdar

    Present address: Department of Computer Science & Engineering, University of Engineering and Management, Area, Plot No. III, B/5, New Town Rd, Action Area III, Newtown, Kolkata, West Bengal, 700160, India

Authors and Affiliations

  1. Department of Computer Science & Engineering, University of Engineering and Management, Area, Plot No. III, B/5, New Town Rd, Action Area III, Newtown, Kolkata, West Bengal, 700160, India

    Biswajoy Chatterjee

  2. Department of Psychology, West Bengal State University, Kolkata, India

    Mallika Banerjee

  3. Department of Computer Science & Engineering, Haldia Institute of Technology, Haldia, India

    Arpita Mazumdar & Sugat Shanker

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  1. Arpita Mazumdar
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Correspondence to Arpita Mazumdar.

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Mazumdar, A., Chatterjee, B., Banerjee, M. et al. Machine learning based autism screening tool—a modified approach. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18519-y

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