A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI

Autism Spectrum Disorder (ASD) is a genetic and neurological condition that leads to difficulties in communication and social interaction. The global concern associated with ASD diagnosis is increasing at a rapid rate due to its significant impact on quality of life. Early identification of ASD can significantly support timely intervention and treatment planning. While research in ASD diagnosis is evolving through the application of machine learning (ML) techniques, practical implementation in clinical settings has not progressed at the same pace. Although theoretical studies have demonstrated improved ML performance, they have not gained much interest among clinicians due to a lack of explainability. This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. The paper uses naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) as classifiers after careful investigation. Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. The RF model achieved up to 99% balanced accuracy on the adult dataset, with similarly strong performance on the children and adolescent datasets using five-fold cross-validation (CV). SHAP are also illustrated to improve model interpretability by highlighting the most influential features, thereby aiding physician understanding. The novelty of this work lies in the integration of explainable AI with robust preprocessing and age-specific modeling across multiple ASD datasets, addressing both diagnostic accuracy and clinical interpretability. It can be considered that the suggested method can efficiently diagnose ASD at a very early stage and enhance the understanding of ASD diagnosis clinically.