Can a machine learning model accurately predict autism spectrum disorder?

In a recent study published in JAMA Network Open , researchers developed and validated four machine learning (ML) models on a dataset comprising more than 30,000 participants to identify a novel ML algorithm (named ‘AutMedAI’) capable of early autism spectrum disorder (ASD) detection with minimal background and medical information. Study: Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information . Image Credit: vetre/Shutterstock.

com Introduction Their findings highlight the eXtreme Gradient Boosting (XGBoost) algorithm as the best-performing ML model for these investigations. Notably, the model significantly outperformed conventional questionnaires and previous artificial intelligence (AI) applications, needing only a minimal set (n = 28) of routine childcare background and medical data for its predictions. This study represents a promising first step in the ideal of early and routine ASD detection, saving patients and their families substantial socioeconomic stress and improving their future quality of life.

Background Autism spectrum disorder (ASD, formerly ‘autism’) is an umbrella term for a diverse group of neurological and developmental conditions that alter patients’ communication, learning, and behavior and may significantly hamper interpersonal communication. Despite decades of research, the diagnosis and treatment of ASD remains an ongoing clinical and psychiatric hurdle. Reports estimate that 1% of al.