Machine Learning–Based Approach for Early Screening of Autism Spectrum Disorders

A neurodevelopmental illness called autism spectrum disorder (ASD) is frequently associated with sensory problems including an excessive or insufficient sensitivity to noises, scents, or touch. Children with autism typically do not talk much and keep to themselves, but they can imitate certain actions from cartoons and movies. They may exhibit unsafe or unexpected behavior as a result. Early detection and therapy can assist in improving the diseases. In this study, we suggested a data‐driven machine learning (ML) model for examining the autism dataset of diverse age groups (toddlers, children, and adults) to classify autism in the initial stage. The proposed ML model can efficiently analyze autism patients’ datasets and correctly classify and detect ASD features. We utilized a data preprocessing technique followed by feature selection methods using information gain and Pearson correlation. Then, we employed five different ML classifiers (KNN, RF, SVM, NB, and MLP) together with a hyperparameter optimization strategy. We assess their work using performance metrics such as prediction accuracy and the F1‐measure. After comparing the accuracy between different classifiers, SVM produced the highest accuracies of 98%, 99%, and 100% for the toddler, child, and adult datasets while MLP produced an accuracy of 0.94 for the Pakistani child dataset, respectively. These thorough experimental assessments suggest that correct fine‐tuning of the ML techniques can be crucial in the classification of autism in individuals of various ages. We believe that the thorough feature significance analysis presented in this study can guide medical professionals’ judgment when screening ASD individuals. In comparison with other methods currently used for the timely identification of ASD, the suggested framework has shown encouraging results.