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Applied Behavior Analysis (ABA) is a research-based approach to behavior modification used in therapeutic interventions for individuals with developmental and behavioral disorders. A critical component of ABA therapy is therapeutic consistency, which can be hindered by provider-related factors such as punctuality, attendance, and engagement. This paper uses a Gradient Boosting Classification Model to assess ABA provider effectiveness using multi-dimensional features derived from a novel dataset. We explore how machine learning, particularly Gradient Boosting, may help create a more predictive model for evaluating provider performance.

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Evaluating Provider Effectiveness in Applied Behavior Analysis Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Austin Weingart,
  • Anthony Andriano,
  • Troy Weingart

摘要

Applied Behavior Analysis (ABA) is a research-based approach to behavior modification used in therapeutic interventions for individuals with developmental and behavioral disorders. A critical component of ABA therapy is therapeutic consistency, which can be hindered by provider-related factors such as punctuality, attendance, and engagement. This paper uses a Gradient Boosting Classification Model to assess ABA provider effectiveness using multi-dimensional features derived from a novel dataset. We explore how machine learning, particularly Gradient Boosting, may help create a more predictive model for evaluating provider performance.