Early Autism Spectrum Disorder Detection : A Multi-Modal Approach with PSO-Driven Ensemble Models

Abstract

Background

The difficulties with social communication and reserve and repetitive behaviors are the symptoms of neurological disorder, known as autism spectrum disorder (ASD). For prompt intervention and better results, early diagnosis of ASD is essential.

Objective

The main aim of this research is to diagnose ASD at early stage using a multi modal dataset that include behavioral, social, and eye contact data to forecast the severity of ASD with the help of machine learning algorithm.

Method

This work investigates the use of particle swarm optimization (PSO) for hyperparameter revising of machine learning models with explainable AI features. One-hot encoding, missing value imputation, and SMOTE were used in the data preprocessing step to solve class imbalance. Hyperparameters were optimized using PSO for Support Vector Machine (SVM). Using stratified k-fold cross-validation, the PSO-optimized models’ performance was contrasted with baseline models. The results show that PSO-based hyperparameter tuning greatly improves ASD level prediction recall and accuracy.

Result

The best result of accuracy 98.60% was obtained by the ensemble model, which combined Random Forest, Gradient Boosting, and PSO-optimized SVM classifiers. This showed how well PSO works to increase the accuracy of ASD diagnoses.

Application

This study can give to the more reliable and precise ASD diagnostic instruments.