Machine learning based autism screening tool—a modified approach

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.