Predictive Analytics & Validation for Technology Intervention Recommendation System for Autism: A Machine Learning Framework
Keywords:
Autism, ASD, Recommendation System, Intervention, TIRS, SVMAbstract
This research aims at development and validation of a Technology Intervention Recommendation System (TIRS) for individuals with Autism. Numerous machine learning (ML) techniques have been used for the development of this system like KNN, Decision Tree, Support Vector Machine (SVM), Naïve Base etc. It has been observed that SVM technique outperform out of other ML techniques. The accuracy of the developed TIRS is found to be 98%, with precision 0.95 and AUC 0.98. TIRS has also been validated with a sample size of 100 (N=100) as compared to clinicians’ predictions. It has been seen that TIRS is predicting interventions with an accuracy of 98%. Moreover, the time taken by TIRS to predict the interventions in 5 seconds only as compared to 5 minutes taken by the clinician.
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