Machine Learning-based ABA treatmentrecommendation and personalization for AutismSpectrum Disorder: An exploratory study

Autism spectrum is a brain development conditionthat impairs an individual’s capacity tocommunicate socially and manifests itself throughstrict routines and obsessive-compulsive behavior.Applied behavior analysis (ABA) is thegold-standard treatment for autism spectrumdisorder (ASD). However, as the number of ASDcases increases, there is a substantial shortage oflicensed ABA practitioners, limiting the timelyformulation, revision, and implementation oftreatment plans and goals. Additionally, thesubjectivity of the clinician and a lack ofdata-driven decision-making affect treatmentquality. We address these obstacles by applying twomachine learning algorithms to recommend andpersonalize ABA treatment goals for 29 studyparticipants with ASD. The patient similarity andcollaborative filtering methods predicted ABAtreatment with an average accuracy of 81-84percent, with a normalized discounted cumulativegain of 79-81 percent (NDCG) compared toclinician-prepared ABA treatmentrecommendations. Additionally, we assess the twomodels’ treatment efficacy (TE) by measuring thepercentage of recommended treatment goalsmastered by the study participants. The proposedtreatment recommendation and personalizationstrategy are generalizable to other interventionmethods in addition to ABA and for other braindisorders. This study has been registered as aclinical trial on November 5, 2020 with trailregistration number CTRI/2020/11/028933