Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI.

ABSTRACT: Background: As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strategies by improving both accuracy and transparency. Methods: This paper presents a method that combines XAI techniques with a rigorous data-preprocessing pipeline to improve the accuracy and interpretability of ML-based diagnostic tools. Our preprocessing pipeline included outlier removal, missing data handling, and selecting pertinent features based on clinical expert advice. Using R and the caret package (version 6.0.94), we developed and compared several ML algorithms, validated using 10

SUBMITTER: Jeon I 

PROVIDER: S-EPMC11592605 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature